{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29e5ccac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Rankings by Different Unified Metrics:\n",
      "                        Model  Pearson  Binary_Accuracy  Unified_Equal  \\\n",
      "1    gemini-2.5-flash-preview   0.4427           0.8300       1.060341   \n",
      "2                    qwen3-8b   0.4459           0.8161       0.976054   \n",
      "3                 gpt-4o-mini   0.4243           0.8167       0.761410   \n",
      "6           claude-3.7-sonnet   0.3898           0.8367       0.578627   \n",
      "0       qwen-2.5-72b-instruct   0.4595           0.7400       0.474734   \n",
      "7               qwen3-30b-a3b   0.3807           0.8267       0.402021   \n",
      "13               gpt-4.1-nano   0.3422           0.8600       0.290345   \n",
      "10           llama-4-maverick   0.3587           0.8233       0.149687   \n",
      "5       deepseek-chat-v3-0324   0.4035           0.7500      -0.010773   \n",
      "4                   qwen3-32b   0.4262           0.7133      -0.088372   \n",
      "8              gemma-3-27b-it   0.3762           0.7600      -0.204380   \n",
      "9                       phi-4   0.3675           0.7700      -0.208812   \n",
      "11       qwen-2.5-7b-instruct   0.3496           0.7167      -0.838869   \n",
      "12               nova-lite-v1   0.3430           0.6810      -1.206064   \n",
      "14  qwen2.5-coder-7b-instruct   0.2662           0.6633      -2.135948   \n",
      "\n",
      "    Unified_Product  \n",
      "1          0.773827  \n",
      "2          0.722163  \n",
      "3          0.637854  \n",
      "6          0.563678  \n",
      "0          0.389934  \n",
      "7          0.492064  \n",
      "13         0.393171  \n",
      "10         0.389247  \n",
      "5          0.313079  \n",
      "4          0.210404  \n",
      "8          0.279758  \n",
      "9          0.284274  \n",
      "11         0.117131  \n",
      "12         0.035752  \n",
      "14         0.000000  \n",
      "\n",
      "Final Unified Rankings:\n",
      "                    Model  Pearson  Binary_Accuracy  Combined Score (Equal Weight)  Combined Score (Product)\n",
      " gemini-2.5-flash-preview   0.4427           0.8300                       1.060341                  0.773827\n",
      "                 qwen3-8b   0.4459           0.8161                       0.976054                  0.722163\n",
      "              gpt-4o-mini   0.4243           0.8167                       0.761410                  0.637854\n",
      "        claude-3.7-sonnet   0.3898           0.8367                       0.578627                  0.563678\n",
      "    qwen-2.5-72b-instruct   0.4595           0.7400                       0.474734                  0.389934\n",
      "            qwen3-30b-a3b   0.3807           0.8267                       0.402021                  0.492064\n",
      "             gpt-4.1-nano   0.3422           0.8600                       0.290345                  0.393171\n",
      "         llama-4-maverick   0.3587           0.8233                       0.149687                  0.389247\n",
      "    deepseek-chat-v3-0324   0.4035           0.7500                      -0.010773                  0.313079\n",
      "                qwen3-32b   0.4262           0.7133                      -0.088372                  0.210404\n",
      "           gemma-3-27b-it   0.3762           0.7600                      -0.204380                  0.279758\n",
      "                    phi-4   0.3675           0.7700                      -0.208812                  0.284274\n",
      "     qwen-2.5-7b-instruct   0.3496           0.7167                      -0.838869                  0.117131\n",
      "             nova-lite-v1   0.3430           0.6810                      -1.206064                  0.035752\n",
      "qwen2.5-coder-7b-instruct   0.2662           0.6633                      -2.135948                  0.000000\n"
     ]
    },
    {
     "data": {
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5ORl//PGHzfonn3zS8PX1NTIyMnKN+amnnjIA4/fff7euu3jxolGxYkUjKCjI+tzIer7WqFHDSE1NtZb98MMPDcDYvXt3rueIj483AOP+++/Ptczlsl6Xjz76qM36Z5991gCMX375xbquQoUKBmAsW7bMpmxWvJUqVbK5j2az2bj77ruNtm3bWp8PhmG51xUrVjTuvfde67qsuj98+LBNuSvl9Hy97777jAoVKmQrm9P7Q2hoqBEYGGicO3fOum7nzp2Gk5OT0bdvX+u6rNfxgAEDbI75wAMPGCVKlMh2rsuZzWajTJkyNq8VwzCMb775xgCM3377zTAMw/j++++t77n5lfUavfKnRo0axl9//WVTNqd7m1WXWbEYhuV9yt3d3XjmmWes61JSUmzeswzDcl/d3d2N119/3bout+eAYRjGp59+agDG3r17revS0tIMf39/o1+/fte81rS0NKNEiRLGiy++aF3Xq1cvIyQkxKbcL7/8YgDG8OHDsx0j6/l34MABw8nJyXjggQeyXdflz9Er/yZlqVChgk3MWfe2adOm2V77OT1/N2zYkO29dcyYMQZgfPfdd7nGvXz5cgMwfvrpJ5vtderUMVq0aJFtPxGxD3W7EBG5Dg8//DDJycksWbKEixcvsmTJkly7XPz44484OzszfPhwm/XPPPMMhmHw008/WcsB2cpd2YrBMAy+/fZbOnXqhGEYnD171vrTtm1b4uPj2b59+3Vd14ABAwgICKB06dLcd999JCYm8vnnn1O/fn1WrFjBhQsX6Nmzp805nZ2dadiwIatXr852vMcffzxP5125ciVpaWk89dRTNv2fBw0ahK+vb7Zm9+7u7kRFReV4rIceegg/Pz/rcsOGDQHo3bu3TVPrhg0bkpaWxvHjx63rLu+bf/HiRc6ePUuzZs1ISkpi3759Nufx8fGx6U/u5uZGeHi4zewg3377Lf7+/jzxxBPZ4jSZTADMnz8fPz8/7r33Xpv7GhYWho+Pj8197d+/P4Zh5LnVw759+wgICCAgIIAaNWowadIk7rvvPmtrj/nz59OsWTOKFStmc+7IyEgyMzP57bffcrw358+fJz4+nmbNmuX4XGvRogU1a9a0WVe0aFESExNtuitd6ccffyQ8PNxmMFcfHx/+97//ERsbS0xMjE35qKgo3NzcrMvNmjUDuOoMLVldHYoUKZJrmStjAhgxYoTN+meeeQYg23OzYsWKtG3bNsdj9evXz+Y+RkdHc+DAAXr16sW5c+es9z8xMZHWrVvz22+/XXVmkvw8X/Pin3/+ITo6mv79+1O8eHHr+jp16nDvvfda78XlHnvsMZvlZs2ace7cOet9zonJZOKhhx7ixx9/5NKlS9b18+bNo0yZMtb6zxoXZsmSJaSnp+f7erJa3KxYsYKffvqJiRMnEh8fT/v27a3djq6mZs2a1ucUWLqKVatWzeb55e7ubn3PyszM5Ny5c/j4+FCtWrUcXxtXPgfA8vfEw8ODOXPmWNctX76cs2fPZhuzIic//fQT586do2fPntZ1PXv2ZOfOnTZd4r799ltMJpO169vlst6PFi5ciNlsZsyYMdnGosgqcz0GDRqUbVyNy+9Deno6586do0qVKhQtWtTm3n377beEhIRka/l0eUyRkZGULl3a5h7u2bOHXbt25ekeisitoW4XIiLXISAggMjISObOnUtSUhKZmZnWgRqvdOTIEUqXLp3tw06NGjWs27N+Ozk5WZviZ7m82TZYZi24cOEC06ZNy3U0/qxBHfNrzJgxNGvWDGdnZ/z9/alRo4b1A/uBAweA/8aBuJKvr6/NsouLi7Vv77Vk3YMrr9XNzY1KlSpZt2cpU6aMzYfOy5UvX95mOSsRUa5cuRzXXz6uwh9//MFLL73EL7/8ku3D05XjLJQtWzbbP+PFihVj165d1uVDhw5RrVo1m6THlQ4cOEB8fDyBgYE5br/eugTLh6//+7//sw4sePfdd9uc58CBA+zatYuAgIBrnnvJkiW88cYbREdH24x1kNMHkooVK2ZbN2TIEL755hvat29PmTJlaNOmDQ8//DDt2rWzljly5Ig1WXS5y18rtWvXtq6/sq6LFSsGkG2sjMtlPU8vXryYa5nLZb0ur5yZpWTJkhQtWjTbczOna89tW9Zrql+/frnuEx8fb72uK+Xn+ZoXub0OwVIHy5cvzzZY4NXq4Mr3hMt1796diRMnsnjxYnr16sWlS5f48ccfGTx4sPU51aJFC7p168Zrr73GBx98QMuWLenSpQu9evXK08wU3t7eREZGWpfbtWtH06ZNqV+/Pm+//Tbvv//+Vfe/8tqyru/y51fWGApTpkzh8OHDNuNtZHVXu1xOz4+iRYvSqVMn5s6dy9ixYwFLN4oyZcrk+n57udmzZ1OxYkXc3d2tY55UrlwZLy8v5syZw1tvvQVY3o9Kly5tk1i60qFDh3BycsqWPLxROV13cnIy48aNY+bMmRw/ftxmDKTLn7+HDh2iW7duVz2+k5MTjzzyCJ988ol1AOI5c+bg4eFh07VHROxLyQcRkevUq1cvBg0axMmTJ2nfvn2Bzt5wNVnfhPbu3TvXDy1Z4yjkV3BwsM0/6zmd98svv7QO2na5Kz9gX/6NYEHLafaALLmNWp/b+qx/eC9cuECLFi3w9fXl9ddfp3Llynh4eLB9+3ZeeOGFbN9AX+t4eWU2mwkMDLT5xu5yuSUG8uLKD185nfvee+/l+eefz3F71apVAfj999/p3LkzzZs3Z8qUKZQqVQpXV1dmzpzJ3Llzs+2XU/0EBgYSHR3N8uXL+emnn/jpp5+YOXMmffv25fPPP7+u67ueOvD19aV06dLs2bMnX+fK67e+V3tuXrkt6zk1fvx4QkNDc9wnt/EZ8vt8vVmu93XQqFEjgoKC+Oabb+jVqxc//PADycnJdO/e3VrGZDKxYMECNm7cyA8//MDy5csZMGAA77//Phs3brzq2BW5CQsLw8/Pz6ZVT27ycm1vvfUWL7/8MgMGDGDs2LEUL14cJycnnnrqqRzrILfnR9++fZk/fz7r168nODiYxYsXM2TIkGu+hyYkJPDDDz+QkpLC3XffnW373LlzefPNN2+o1UJ+XDk4b5acrvuJJ55g5syZPPXUUzRu3Bg/Pz9MJhM9evS4rudv3759GT9+PAsXLqRnz57MnTuXjh072rSEExH7UvJBROQ6PfDAAwwePJiNGzcyb968XMtVqFCBlStXcvHiRZvWD1nNoitUqGD9bTabrd+WZ9m/f7/N8bJmwsjMzLzqB8uCltUiIzAwsMDPm3UP9u/fT6VKlazr09LSOHz48C25zjVr1nDu3Dm+++47mjdvbl1/+Uwf+VW5cmU2bdpEenp6rtNaVq5cmZUrV9KkSZOrfnC9GSpXrsylS5eueX+//fZbPDw8WL58uc03zjNnzszX+dzc3OjUqROdOnXCbDYzZMgQPv30U15++WWqVKlChQoVsj3fIftr5UZ17NiRadOmsWHDBho3bnzVslmvywMHDlhbYIBlcMwLFy7cUExZrylfX998P8fz83zN6wfPy1+HV9q3bx/+/v4FOkXiww8/zIcffkhCQgLz5s0jKCiIRo0aZSvXqFEjGjVqxJtvvsncuXN55JFH+Prrr697KsnMzEyb7h43YsGCBbRq1Yrp06fbrL9w4QL+/v55Pk67du0ICAhgzpw5NGzYkKSkJPr06XPN/b777jtSUlL45JNPsp1v//79vPTSS6xbt46mTZtSuXJlli9fTlxcXK6tHypXrozZbCYmJibXhBhYWoBcORNRWloa//zzzzVjzrJgwQL69etn0wIlJSUl23ErV66cp2Rh7dq1qVu3LnPmzKFs2bIcPXqUSZMm5TkeEbn5NOaDiMh18vHx4ZNPPuHVV1+lU6dOuZbr0KEDmZmZfPzxxzbrP/jgA0wmk3VmhKzfV86WcfmMA2D5Nq5bt258++23Of5Dlpe+zNejbdu2+Pr68tZbb+XY//pGzhsZGYmbmxsfffSRzbeK06dPJz4+PscZPwpa1recl58/LS2NKVOmXPcxu3XrxtmzZ7PV/eXnefjhh8nMzLQ2t75cRkaGzT/iNzrV5pUefvhhNmzYwPLly7Ntu3DhAhkZGYDl3phMJptvNWNjY1m4cGGez3Xu3DmbZScnJ2sLnaxuHB06dGDz5s1s2LDBWi4xMZFp06YRFBRUYE3Bn3/+eby9vXn00Uc5depUtu2HDh2yTgHaoUMHIPvrcMKECQA39NwMCwujcuXKvPfeezl+GL7aayo/z1dvb+88PWdKlSpFaGgon3/+uc3zbs+ePfz888/We1FQunfvTmpqKp9//jnLli3j4Ycfttl+/vz5bC0osj4QXznNaV6tXr2aS5cuERIScl37X8nZ2TlbjPPnz7cZSyYvXFxc6NmzJ9988w2zZs0iODg4Ty3YZs+eTaVKlXjsscd48MEHbX6effZZfHx8rK2qunXrhmEYvPbaa9mOk3UNXbp0wcnJiddffz1b64PLr7Ny5crZWo9MmzYt15YPOcnp3k2aNCnbMbp168bOnTuzzSxzZUwAffr04eeff2bixImUKFHC+ndVRG4PavkgInIDrtZXO0unTp1o1aoVL774IrGxsYSEhPDzzz+zaNEinnrqKeu3n6GhofTs2ZMpU6YQHx9PREQEq1atsvbhvdzbb7/N6tWradiwIYMGDaJmzZrExcWxfft2Vq5cSVxcXIFfq6+vL5988gl9+vShXr169OjRg4CAAI4ePcrSpUtp0qRJjh+y8yIgIIBRo0bx2muv0a5dOzp37sz+/fuZMmUKDRo0uCUDhkVERFCsWDH69evH8OHDMZlMfPnll/nuRnG5vn378sUXXzBixAg2b95Ms2bNSExMZOXKlQwZMoT777+fFi1aMHjwYMaNG0d0dDRt2rTB1dWVAwcOMH/+fD788EPreCIFMdXm5Z577jkWL15Mx44drVMIJiYmsnv3bhYsWEBsbCz+/v7cd999TJgwgXbt2tGrVy9Onz7N5MmTqVKlis0YF1fz6KOPEhcXxz333EPZsmU5cuQIkyZNIjQ01NqiYOTIkdYpbIcPH07x4sX5/PPPOXz4MN9++22BdeOpXLkyc+fOpXv37tSoUYO+fftSu3Zt0tLSWL9+PfPnz7fe35CQEPr168e0adOsXR02b97M559/TpcuXWjVqtV1x+Hk5MRnn31G+/btqVWrFlFRUZQpU4bjx4+zevVqfH19+eGHH3LcNz/P17CwMObNm8eIESNo0KABPj4+uSZMx48fT/v27WncuDEDBw60TrXp5+fHq6++et3XmpN69epRpUoVXnzxRVJTU226XAB8/vnnTJkyhQceeIDKlStz8eJF/u///g9fX988JULi4+OZPXs2YEnkZU3f6+npyciRIwvkGjp27Mjrr79OVFQUERER7N69mzlz5ti04Mqrvn378tFHH7F69eqrTlWa5cSJE6xevTrbIMVZ3N3dadu2LfPnz+ejjz6iVatW9OnTh48++ogDBw7Qrl07zGYzv//+O61atWLYsGHW+hg7dizNmjWja9euuLu7s2XLFkqXLs24ceMAy+v5scceo1u3btx7773s3LmT5cuX56u1R8eOHfnyyy/x8/OjZs2abNiwgZUrV2YbK+O5555jwYIFPPTQQwwYMICwsDDi4uJYvHgxU6dOtUkk9erVi+eff57vv/+exx9/PNcWZyJiJ7dwZg0REYd2+VSbV3PlVJuGYZku8OmnnzZKly5tuLq6Gnfffbcxfvx4m6nLDMMwkpOTjeHDhxslSpQwvL29jU6dOhl///13jtOanTp1yhg6dKhRrlw5w9XV1ShZsqTRunVrY9q0adYy+Z1qc/78+de8D6tXrzbatm1r+Pn5GR4eHkblypWN/v37G1u3brWW6devn+Ht7Z3j/jlNtZnl448/NqpXr264uroad911l/H4448b58+ftynTokULo1atWtn2zbrW8ePH5+nacqrPdevWGY0aNTI8PT2N0qVLG88//7x1CrfVq1dfM4Z+/fplm9IwKSnJePHFF42KFSta6+nBBx80Dh06ZFNu2rRpRlhYmOHp6WkUKVLECA4ONp5//nnjxIkT2WLO61SbOcV4pYsXLxqjRo0yqlSpYri5uRn+/v5GRESE8d577xlpaWnWctOnTzfuvvtuw93d3ahevboxc+ZMa11eDjCGDh2a7TwLFiww2rRpYwQGBhpubm5G+fLljcGDBxv//POPTblDhw4ZDz74oFG0aFHDw8PDCA8PN5YsWWJTJrc6zevzPcuff/5pDBo0yAgKCjLc3NyMIkWKGE2aNDEmTZpkM1Vlenq68dprr1nrsFy5csaoUaNsyhhGzq/9q8WbZceOHUbXrl2NEiVKGO7u7kaFChWMhx9+2Fi1apW1TE7TQeb1+Xrp0iWjV69eRtGiRQ3A+hzN7X6tXLnSaNKkieHp6Wn4+voanTp1MmJiYmzK5PY6zinOq3nxxRcNwKhSpUq2bdu3bzd69uxplC9f3nB3dzcCAwONjh072rzX5ObKqTZNJpNRvHhxo3Pnzsa2bduuGXNuddmiRQubqRtTUlKMZ555xihVqpTh6elpNGnSxNiwYUO2cnl9j61Vq5bh5ORkHDt27JrX+P777xuAzfPkSrNmzTIAY9GiRYZhGEZGRoYxfvx4o3r16oabm5sREBBgtG/fPts9mTFjhlG3bl3D3d3dKFasmNGiRQtjxYoV1u2ZmZnGCy+8YPj7+xteXl5G27ZtjYMHD+Y61WZOfzfPnz9vREVFGf7+/oaPj4/Rtm1bY9++fdmOYRiGce7cOWPYsGFGmTJlDDc3N6Ns2bJGv379sk03bRiG0aFDBwMw1q9ff817KCK3lskwbuArHRERERERKRB169alePHirFq1yt6hOKwHHniA3bt359hqUETsS2M+iIiIiIjY2datW4mOjqZv3772DsVh/fPPPyxdujRPg3WKyK2nlg8iIiIiInayZ88etm3bxvvvv8/Zs2f566+/8PDwsHdYDuXw4cOsW7eOzz77jC1btnDo0KEcp4QWEftSywcRERERETtZsGABUVFRpKen89VXXynxcB1+/fVX+vTpw+HDh/n888+VeBC5Tanlg4iIiIiIiIjcVGr5ICIiIiIiIiI3lZIPIiIiIiIiInJTudg7ACkYZrOZEydOUKRIEUwmk73DERERERERkULOMAwuXrxI6dKlcXK6etsGJR8KiRMnTlCuXDl7hyEiIiIiIiJ3mL///puyZctetYySD4VEkSJFAEul+/r62jkax2Q2mzlz5gwBAQHXzNrJ7UF15phUb45HdeZ4VGeOSfXmeFRnjkd1VrASEhIoV66c9fPo1Sj5UEhkdbXw9fVV8uE6mc1mUlJS8PX11RuRg1CdOSbVm+NRnTke1ZljUr05HtWZ41Gd3Rx56fqvuy0iIiIiIiIiN5WSDyIiIiIiIiJyUyn5ICIiIiIiIiI3lZIPIiIiIiIiInJTKfkgIiIiIiIiIjeVkg8iIiIiIiIiclMp+SAiIiIiIiIiN5WSDyIiIiIiIiJyUyn5ICIiIiIiIiI3lZIPIiIiIiIiInJTKfkgIiIiIiIiIjeVkg8iIiIiIiIiclMp+SAiIiIiIiIiN5WSDyIiIiIiIiJyUyn5ICIiIiIiIiI3lZIPIiIiIiIiInJTKfkgIiIiIiIiIjeVkg8iIuKQJk6cyMmTJ69ZLjk5mZo1axIaGpprmRkzZhAcHIyLiwsTJ04suCBvgGEYxCWm8XdcEnGJaRiGYe+QRERERK6bi70DEBERuR4TJ06kZcuWlCxZ8qrlXnjhBZo0acKWLVtyLRMWFsY333zDuHHjCjrMfItPTufbbcf4fH0sR+KSrOsrFPeiX0QQ3cLK4ufpascIRURERPJPLR9ERMTuFi1aRI0aNQgJCeGFF17A39+f2NhYgoKCeO655wgLC6NKlSqMHz8egNdff50TJ07QvXt3QkNDiY6OzvG4K1eu5Pjx4zzyyCNXPX9ISAg1atTAyenafxZbtmzJs88+S7NmzahcuTKPPfaYddvcuXNp2LAhdevWJSQkhB9++CFP+50+fZquXbtSqVpN7qpwN8+/8T5HL0s8AByNS2Lskhgaj1vFr3+euWacIiIiIrcTtXwQERG7On36NAMGDGDdunVUr16dmTNncu7cOev2U6dOsXXrVs6dO0e9evVo0qQJY8aMYcaMGcybNy/X7hQXLlzg+eefZ9myZcTExBRozIcOHWL16tWkp6dTs2ZNNmzYQOPGjWnbti09e/bEZDIRGxtLo0aNOHLkCO7u7lfd74knnsAzoBx0HUhg4gVOzHoK14CKuJepbj1nVqeL5PRMomZuZmZUOC2qBhTodYmIiIjcLGr5ICIidrVx40bq1KlD9eqWD9r9+vXDzc3Nun3gwIGYTCb8/f3p2rUrK1euzNNxhw0bxujRowkMDCzwmLt3746Liwuenp6EhoZy6NAhAA4fPkz79u2pXbs2Xbp0IS4ujsOHD19zvxUrV7LJvR4G4ORVFK+qjUk+Ep3juQ3Dkoh4fPY24pPTC/zaRERERG4GJR9ERMShmEymHNdHREQQGhpKw4YNAVi7di3PPvssQUFB9OjRg5iYGKpVq5avc8XExBAaGkpoaChDhw61rvfw8LA+dnZ2JiMjA4AePXrw6KOPsmfPHqKjo/Hx8SElJeWa+6VlmElJz+S/MSVzvsYshgHJaZl8t/1Yvq5HRERExF6UfBAREbtq1KgRu3btYv/+/QDMnj2btLQ06/ZZs2YBEBcXx/fff0/r1q0B8PX1JT4+3lpu/fr1REdHs2nTJgBiY2OtP19//TU1a9a0niOvatasSXR0NNHR0UyePPma5c+fP0/FihWt13H+/Plr7mMYBl5BoVzauRyAzKR4kv7cgGdQ3WvuO2tdrGbBEBEREYeg5IOIiNhVYGAgn332GV26dCE0NJTdu3fj4+ND0aJFAQgICCAsLIzw8HCGDRtGREQEAMOHD2fQoEFXHXAyNydOnLAZK2LWrFmULVuW+fPn8+qrr1K2bFl27NiR72v58MMPefDBB6lbty47duygfPny19znfFI67s0Hknbub05MH8qpr0bhF/Ew7qWv3krDAI7EJXEhSV0vRERECo20NEgvnH/bTYa+MikUEhIS8PPzIz4+Hl9fX3uH45DMZjOnT58mMDAwTyPei/2pzhxTTvV28eJFihQpAsDChQsZNWoUe/fuJSgoiIULF+Y6qGRh8HdcEs3eXX3d+//+fCvKFfcqwIiy02vN8ajOHJPqzfGozhzPbV1nsbGwaBFUrw5t29o7mjzJz+dQzXYhIiJ2N2nSJObNm0dmZia+vr7MmTPH3iHdMt7uN/an2OcG9xcRERE7S0uDFStgyxbL8t690KoVXDYAd2Gg/1hERMTuRo8ezejRo7Otj42NvfXB3GLFvFypUNyLo3FJ5KcpogkoX9yLol6uNys0ERERudn++gsWL4YLFyzLYWHQpk2hSzyAkg8iIiJ2ZTKZ6BcRxNglMfnet3+ToFxn/xAREZHbWGqqpbXD1q2W5aJFoXNnqFTJrmHdTLdZJxcREZE7T7ewsni6OZPXPIKTCTzdnOlar+zNDUxERERujtRU2LPH8rhBA3j88UKdeAC1fBAREbE7P09XPukdRtTMzWCCqw0FnZWgmNo7DD9PdbkQERFxGBkZ4PLvR3BfX0tLB09P+Hea7sJOLR9ERERuAy2qBjAzKhxPV2dMWMZ0uFzWOk9XZ2ZFhdO8asCtD1JERESuz4ED8NFHlt9Zata8YxIPoJYPIiIit40WVQPYMKo1320/xqx1sRyJS7JuK1/ci/5NgugWVhZfD7V4EBERcQjJybB8OURHW5bXrYO777ZrSPai5IOIiMhtxM/TlagmFekfEcSFpHQupWbg4+5CUS9XDS4pIiLiSP78E374AS5etPSbbNQI7rnH3lHZjZIPIiIityGTyUQxbzeKeRe+qbZEREQKteRkWLYMdu60LJcoAfffD+XL2zcuO1PyQURERERERKSgHDliSTyYTNC4MbRqBa7qMqnkg4iIiIiIiMiNMIz/pqSqXh2aNoVq1aBcOfvGdRvRbBciIiIiIiIi12vvXpgyBS5d+m9dZKQSD1dQywcRERERERGR/EpMhJ9+gj17LMtr10K7dvaN6Tam5IOIiIiIiIhIfvzxB/z4oyUB4eQETZpAixb2juq2puSDiIiIiIiISF5cumRJOsTEWJYDA6FLFyhd2q5hOQIlH0RERERERETyYv16S+LByQmaNYPmzcHZ2d5ROQQlH0RERERERETyokULOHcOWraEUqXsHY1D0WwXIiIiIiIiIlcyDNi1C775xvIYwN0devZU4uE6qOWDiIiIiIiIyOUuXoQffoA//7Qs79kDwcH2jcnBKfkgIiIiIiIiApYWDjt3wrJlkJJiGc+hRQuoWdPekTk8JR9EREREREREEhIsrR0OHLAsly5tmckiMNCuYRUWSj6IiIiIiIiIzJ8Pf/9tae3QqhVERFhmtZACoeSDiIiIiIiISLt2lu4WnTtDQIC9oyl0lMbJweTJkwkKCsLDw4OGDRuyefPmq5afOHEi1apVw9PTk3LlyvH000+TkpJi3f7qq69iMplsfqpXr25zjJSUFIYOHUqJEiXw8fGhW7dunDp16qZcn4iIiIiIyB3NMGDbNti48b91ZcrAgAFKPNwkavlwhXnz5jFixAimTp1Kw4YNmThxIm3btmX//v0E5tDXZ+7cuYwcOZIZM2YQERHBn3/+Sf/+/TGZTEyYMMFarlatWqxcudK67OJie+uffvppli5dyvz58/Hz82PYsGF07dqVdevW3byLFRERERERucOY4uMtLRxiYy1dLO6+G0qU+Hejya6xFWZKPlxhwoQJDBo0iKioKACmTp3K0qVLmTFjBiNHjsxWfv369TRp0oRevXoBEBQURM+ePdm0aZNNORcXF0qWLJnjOePj45k+fTpz587lnnvuAWDmzJnUqFGDjRs30qhRo4K8RBERERERkTuPYcCWLXgtXIjJzQ3c3OCee6BYMXtHdkdQ8uEyaWlpbNu2jVGjRlnXOTk5ERkZyYYNG3LcJyIigtmzZ7N582bCw8P566+/+PHHH+nTp49NuQMHDlC6dGk8PDxo3Lgx48aNo3z58gBs27aN9PR0IiMjreWrV69O+fLl2bBhQ47Jh9TUVFJTU63LCQkJAJjNZsxm8/XfhDuY2WzGMAzdPweiOnNMqjfHozpzPKozx6R6czyqMwdy/jwsXgyHD0NaGubKlTHdf/9/LR5Uh9clP899JR8uc/bsWTIzM7nrrrts1t91113s27cvx3169erF2bNnadq0KYZhkJGRwWOPPcbo0aOtZRo2bMisWbOoVq0a//zzD6+99hrNmjVjz549FClShJMnT+Lm5kbRokWznffkyZM5nnfcuHG89tpr2dafOXPGZrwJyTuz2Ux8fDyGYeCkUW0dgurMManeHI/qzPGozhyT6s3xqM4cRFoa3v/3f5iSkzE7O3OuQQPcmzXDKTMTTp+2d3QO7eLFi3kuq+TDDVqzZg1vvfUWU6ZMoWHDhhw8eJAnn3ySsWPH8vLLLwPQvn17a/k6derQsGFDKlSowDfffMPAgQOv67yjRo1ixIgR1uWEhATKlStHQEAAvr6+N3ZRdyiz2YzJZCIgIEB/PByE6swxqd4cj+rM8ajOHJPqzfGozhxImzZw4ADmjh1JzMxUnRUQDw+PPJdV8uEy/v7+ODs7Z5tl4tSpU7mO1/Dyyy/Tp08fHn30UQCCg4NJTEzkf//7Hy+++GKOT+iiRYtStWpVDh48CEDJkiVJS0vjwoULNq0frnZed3d33N3ds613cnLSi+gGmEwm3UMHozpzTKo3x6M6czyqM8ekenM8qrPbkNkMmzdD2bKWH4AmTaBpUzAMTKdPq84KSH7uoe72Zdzc3AgLC2PVqlXWdWazmVWrVtG4ceMc90lKSsp2w52dnQEwDCPHfS5dusShQ4coVaoUAGFhYbi6utqcd//+/Rw9ejTX84qIiIiIiMgVzp2DWbMss1ksWgQZGZb1Tk6aycLO1PLhCiNGjKBfv37Ur1+f8PBwJk6cSGJionX2i759+1KmTBnGjRsHQKdOnZgwYQJ169a1drt4+eWX6dSpkzUJ8eyzz9KpUycqVKjAiRMneOWVV3B2dqZnz54A+Pn5MXDgQEaMGEHx4sXx9fXliSeeoHHjxprpQkRERERE5FrMZti4EX75xZJwcHODhg0tU2nKbUHJhyt0796dM2fOMGbMGE6ePEloaCjLli2zDkJ59OhRm5YOL730EiaTiZdeeonjx48TEBBAp06dePPNN61ljh07Rs+ePTl37hwBAQE0bdqUjRs3EhAQYC3zwQcf4OTkRLdu3UhNTaVt27ZMmTLl1l24iIiIiIiIIzp7FhYuhGPHLMuVK0OnTnDFgP5iXyYjt74B4lASEhLw8/MjPj5eA05eJ7PZzOnTpwkMDFT/LwehOnNMqjfHozpzPKozx6R6czyqs9vAqVPwf/9nae3g7g5t20Ldurl2sVCdFaz8fA5VywcRERERERFxTIGBUL68ZUyHTp3Az8/eEUkulHwQERERERERx5A1k0XdupaWDiYTdO9uGeNBA0re1pR8EBERERERkdvfqVOWGSxOnLCM89Cxo2W9u7t945I8UfJBREREREREbl+ZmbB2Lfz2m+WxhweUK2fvqCSflHwQERERERGR29PJk5bWDv/8Y1muVs3S4qFIEfvGJfmm5IOIiIiIiIjcfmJiYMECyzgPnp7QoQPUrq2xHRyUkg8iIiIiIiJy+6lQwdLFokIFuO8+8PGxd0RyA5R8EBEREREREfvLyIB9+yytGwC8veGxxyxdLNTaweEp+SAiIiIiIiL2deIELFwIp0+DkxPUrGlZ7+tr17Ck4Cj5ICIiIiIiIvaRkQFr1sD69ZaxHby9wdnZ3lHJTaDkg4iIiIiIiNx6x45ZZrI4c8ayXLu2ZVBJLy/7xiU3hZIPIiIiIiIicmutXw8rVoBhWAaS7NgRqle3d1RyEyn5ICIiIiIiIrdWYKAl8VCnDrRvb5lKUwo1JR9ERERERETk5kpPh5MnoVw5y3KVKpaZLEqWtG9ccsso+SAiIiIiIiI3z5EjlrEdEhNhyBDw87OsV+LhjqLkg4iIiIiIiBS8tDRYtQo2b7Z0sfD1hYSE/5IPckdR8kFEREREREQKVmyspbXD+fOW5Xr1oE0b8PCwa1hiP0o+iIiIiIiISMEwDFi2DDZtsiz7+UGnTpYxHuSOpuSDiIiIiIiIFAyTCZydLY/DwiytHdzd7RuT3BaUfBAREREREZHrl5oKKSn/jeXQqhVUrQpBQXYNS24vSj6IiIiIiIjI9Tl0CBYvhiJFYMAAcHICV1clHiQbJR9EREREREQkf1JSYMUK2LbNsuzkZJnJomhRu4Ylty8lH0RERERERCTvDh60tHZISLAsN2wIrVuDm5t945LbmpIPIiIiIlKoGIbB+aR0ElMz8HZ3oZiXKyaTyd5hiTi+tDT46SfYscOyXLw43H8/VKhg37jEISj5ICIiInKTBQUFsXDhQkJDQwvsmA8++CAdO3akf//++drvxRdfZPHixTj/Oxr9yJEj6dGjR7Zyu3fvpk+fPtblCxcukJCQQFxc3A3FfTP9vnELs5dtYJ9XMEfikqzrKxT3ol9EEN3CyuLn6WrHCEUcnIsLnDplmdEiq7WDq15TkjdKPoiIiIjcQZ577jnefPNNAI4fP06NGjWIjIzE39/fplxwcDDR0dHW5WHDht3WrQd+/fMMPcd9zYW967mra2WbbUfjkhi7JIb3ft7PJ73DaFE1wE5Rijig5GRLgsHFxTKuQ5culvEeype3d2TiYJzsHYCIiIhIYbJhwwaaNm1KSEgIderUYdGiRTbbJ0yYQIMGDQgNDaVBgwZs2LDBui0oKMjmA3/9+vVZs2YNAPv27SMiIoJatWrRpUsXErL6WgMXL15k0KBBhIeHU6dOHQYPHkxaWlqO8RW9bDC4S5cuYRgGZrP5qteUkpLCnDlzGDhwYI7bk5OT6d69OzVr1iQkJIQ2bdpYt40fP55atWoRHBzMI488Qnx8PACvvvoq3bt3p1OnTtSsWZN77rnH2qpi1qxZREZG0rNnT4KDg6lfvz5//fWX9ZhffvklDRs2pF69ejRv3pzpi1fT9+OfOb3mS1KO7OL4zCc4t/xja3nj35/k9EyiZm7m1z/PXPV6ReRf+/fD5Mnw22//rQsMVOJBrouSDyIiIiIFJC4uji5dujBu3Dh27txJdHQ0zZo1synTp08ftmzZQnR0NJMmTSIqKipPx+7Tpw8DBw7kjz/+YOzYsfz666/Wbc888wzNmjVj8+bN7Ny5E7PZzGeffZbrsT766COqVatGvXr1mDZtGoGBgVc993fffUelSpVy7TaybNkyLly4QExMDDt37uTrr78G4KeffmLGjBmsW7eO3bt34+3tzciRI637bdq0iVmzZhETE0NgYCCffvqpdduWLVt466232L17N5GRkbzzzjsArFu3jq+++orffvuN7du3M2rMawwZ2B8n76IUbfoIHhXqUDpqEiXaDssWp2FYkhCPz95GfHL6Va9Z5I6WlATffgtffQWXLsG+fZCZae+oxMGp24WIiIhIAdmwYQPVqlWzJhycnJwoXry4TZkdO3bw5ptvcu7cOVxcXNi/fz/Jycl4enrmetyEhASio6Ot4zsEBwfTtGlT6/aFCxeyYcMGJkyYAFhaIoSHh+d6vOHDhzN8+HB27txJ7969adOmDSVKlMi1/PTp03Nt9QAQEhLC3r17GTJkCC1atKBDhw4ArFy5ku7du1tbWzz++OM89NBD1v3atWtnPW/jxo3ZvXu3dVvjxo2pWLGi9fGkSZMAWLRoETt37qRhw4YAxCWmkZl8kcy01Fzju5xhQHJaJt9tP0ZUk4p52kfkjrJ3LyxZAomJlrEdmjSBli3h33FiRK6XWj6IiIiI3CJpaWl07dqV9957jz179vDbv02ZU1MtH5xdXFzIvOzbxZSUlFyPdfn4C4Zh8O233xIdHU10dDR79+5l/PjxxMTEEBoaSmhoKEOHDs12jJCQEMqUKWPt2pGTw4cPs3HjRnr16mVd98UXX1iPO3PmTCpVqkRMTAzt2rVj3bp11K5dm/Pnz181ZgAPDw/rY2dnZzIyMq65zTAM+vXrR3R0NDt27KDioMmUG/YlTq7uuV5DTmati8UwjHztI1KoJSXBggUwb54l8RAQAI8+CpGRlvEeRG6Qkg8iIiIiBSQiIoIDBw7w+++/A2A2m21mh0hJSSEtLY3y//aXzvo2P0uVKlXYtGkTAJs3b2b//v0A+Pr6UrduXb744gsA/vjjD9auXWvdr0uXLrzzzjvWD+jnz5/n8OHD1KxZ05qQmDx5MgAxMTHW/Q4dOsSOHTuoWbNmrtc0Y8YMHnjgAZuxIvr27Ws9blRUFMeOHcNkMtG5c2fee+89DMPg77//JjIykm+++cY6PsWnn35qMx7E9ejcuTOzZ8/m6NGjnE9KJ/bcJVL+OQCAk5sX5tTEax7DAI7EJXEhSV0vRKxSU+HPPy2DSjZrBoMHQ5ky9o5KChGlsEREREQKSLFixfj+++955plnuHjxIk5OTowdO9a63dfXlzfeeIPw8HD8/f2zTXH5xhtv0K9fPz799FMaN25MrVq1rNu++OILoqKieP/997n77rtp3ry5ddsHH3zAyJEjCQ0NxcnJCRcXF0aOHGntmnC5559/nsOHD+Pq6oqLiwsff/wxNWrUAGDq1KmcOHGC119/HbAkT2bNmmVNeuRm9+7djBo1CsMwyMjIoE+fPtSpU4c6deqwZ88eGjdujJOTE3Xq1GHKlCn5v7GXadasGe+++y4PPPAAyalpnPjnAl6VG+Be6m48gkJI2PwdJ2YMw71M9RzHfbjcpdQMinm73VA8Ig4tLQ3c/n0NFCsGnTtD8eJQurR945JCyWSovVmhkJCQgJ+fH/Hx8fj6+to7HIdkNps5ffo0gYGBODmpUZAjUJ05JtWb41GdOZ47pc7iEtOoN3bFde+/4+V7b6vkw51Sb4WJw9aZYcAff8BPP8GDD0LFO2f8E4ets9tUfj6HquWDiIiIiDikYl6uVCjuxdG4JPLzbZoJKF/ci6JerjcrNJHb16VLsHSpZWBJgE2b7qjkg9iPUj0iIiIi4pBMJhP9IoKua9/+TYKyDYApUqgZBuzaBZMnWxIPTk7QqhVcNgONyM2klg8iIiIi4rC6hZXlvZ/3k5yeSV46EzuZwMPVma71yt784ERuFxcvWqbP/HcQW0qVgvvvh5Il7RuX3FHU8kFEREREHJafpyuf9A7DBFyrIUPW9qm9w/DzVJcLuYPExloSD87OcM89lik0lXiQW0wtH0RERETEobWoGsDMqHAen72N5LRMAJsxILJyEp6uzkztHUbzqgG3PEaRW85stnStAKhdG06dgjp1IDDQvnHJHUvJBxERERFxeC2qBrBhVGu+236MWetiORKXZN1WvrgX/ZsE0S2sLL4eavEghZxhQHQ0rFsHAwaAl5el2U9kpL0jkzuckg8iIiIiUij4eboS1aQi/SOCuJCUzqXUDHzcXSjq5arBJeXOEB8PP/wABw9aljdtsgwqKXIbUPJBRERERAoVk8lEMW83inm72TsUkVvDMGD7dvj5Z0hNBRcXS9KhcWN7RyZipeSDiIiIiIiIo7pwARYvhr/+siyXK2eZycLf365hiVxJyQcRERERERFHtXatJfHg4gKtW0PDhv8NNHmbMQyD80npJKZm4O3uQjF1ibqj3J7PShERERERkSucOHGCZs2a5alshw4d2L9/f47bRo8eTfXq1QkJCaF+/fosX7481+MEBQVRrVo1QkNDCQ0NZd68ebmWXbRoETVq1CA0NJTdu3djMpm4cOFCnuK90po1awgNDb12wdatoWZNePxxSzeLm5R42Lp1K927d7+ufeOT05mx9jAtx6+h3tgVNHt3NfXGrqDl+DXMWHuY+OT0Ao5Wbkdq+SAiIiIiIg6hdOnS/P7773kq++OPP+a6rVmzZrz88st4enqyc+dOmjdvzokTJ/D29s6x/Lx58/KUCJg6dSpjxoyhZ8+eeYox3wwDNm+Go0fhwQcts1h4esLDD+f5EBkZGbi45P9jYP369a+aeMnNr3+esZkG93JH45IYuySG937ezye9w2ihaXALNbV8EBERERGRApP17X9ISAgvvPAC/v7+xMbGcuDAAe677z4aNGhAnTp1+Pjjj637mEwm3nzzTRo2bEhQUBALFy5k3Lhx1K9fn7vvvps1a9YAEBsbS9GiRW32e+uttwgPD6dixYrMnDnTui0oKIjo6OgcY2zfvj2enp4ABAcHYxgGZ86cuaHrHj58OL///jujR48mIiIi2/Znn32WBg0aEBoaSvPmza2tMpKTk+nevTs1a9YkJCSENm3aWPfJyMhgyJAhhISEUKt6dba+8gr89BP88QccOGAtl3Vfnn32WerUqUOtWrVYuXKlzbYXXniBevXq8fHHH3Py5EkefvhhwsPDCQ4O5qWXXgJgzpw5dOzY0XpcwzCoVKkSO3fuzNYSY/ny5TRt2pSwsDDCw8NZvXo1AL169WLu3LkAPP3KO7SsUZrExEQM4J+vRpPy957/jv/vT3J6JlEzN/PrnzdWB3J7U/JBREREREQKxOnTpxkwYADff/89O3fupHr16pw7d47MzEx69uzJ+++/z5YtW9i4cSPTpk1jy5Yt1n19fHzYtGkT06dPp3fv3pQqVYqtW7fy1ltv8dxzz+V6Tnd3dzZv3sxPP/3E8OHDycjIyFfMM2fOpFKlSlSoUCHXMn379iU4OJiBAwfmmqT46KOPqF+/Ph988AHr16/Ptv2FF15gy5YtREdHM2TIEJ588kkAli1bxoULF4iJiWHnzp18/fXX1n327dtHvz592PnppzxRrRovzp0Lbm5w331w9902x4+Pj6dGjRrs2rWL6dOn06tXLy5evGjdVqtWLbZv385TTz1Fv379GDp0KJs3b2bHjh1s3bqV+fPn07VrVzZu3MjJkycBS9ePYsWKERISYnOuv/76i1dffZUff/yRbdu2MXfuXHr16kVqaiqRkZGsXLmS+OR0Ppu3CPdSVUg5uhtzegrppw/jXrp6tntjGJYkxOOzt6kLRiGm5IOIiIiIiBSIjRs3UqdOHapXt3zA7NevH25ubqSmpvLHH3/Qo0cPQkNDiYiI4OLFi8TExFj3zRpPoH79+iQmJtKjRw8AwsPDOXDZt/xXeuSRRwCoXr06Li4u1g/OebFq1Spee+015s2bl+vAh7/99hu7du1i+/bt+Pv7069fvzwf/3IrVqygcePG1K5dm9dff93aKiMkJIS9e/cyZMgQ5s2bh6urq3WfKhUr0nDvXli2jMalSnHo4kXL2A4NGli6XFzGxcWF/v37A9CoUSNKly7Njh07AHB1daV3794AJCYmsmrVKp588klCQ0OpX78+Bw8eZP/+/Xh6etKtWze+/PJLAGbNmkVUVFS2a1m2bBkHDx6kefPmhIaG8uCDD+Lk5MTRo0eJjIxk1apVzN98hJQzRylSvwvJsdGk/v0HbqWqYnLOucuHYUByWibfbT92XfdXbn8a80FERERERG4qwzAoXrx4rt0gADw8PABwdnbOtny11gxZ5XIrGxMTQ69evQBo0qQJkydPBuDXX38lKiqKH374gWrVquV6/PLlywOWD/BPPfUUVatWBeCLL75gwoQJZGRk8PTTTzNw4MBcj3H06FGGDRvGli1bqFy5Mrt27aJ58+YAVKpUiZiYGH755RdWrlzJ888/b7lPhoFHSoplfAc3N5xbtiRj2TIoVoy3337b2kLinXfeyTX+rISKl5cXTv8ORGkYBmBJFF1+77IMGDCAqKgoHn/8cZYsWcIHH3yQrYxhGNx7773W7hVXcnd3570p03G/qwoeQaHEb/gGk5MzHhVCcix/uVnrYukfEaRZMAohtXwQEREREZEC0ahRI3bt2mUdz2D27NmkpaXh7u6Or6+vzZgMBw8eJC4u7qbHVLNmTaKjo4mOjrYmHn777Tf69OnDokWLsnUpuFxiYqLNbBVfffUVdevWBSxdMbZv387KlStzbB1wufj4eFxdXSlVqhSGYdiMd3Hs2DFMJhOdO3fmvffewzAM/v77b0vLhiJFoHJlGDIEate27jNy5EjrNbVt2xawjA+R1WJh8+bNnDhxIsdBMn18fGjVqhVvv/22dd2JEyc4dszS4qBhw4aAZYyKyMhIihcvnu0Ybdu2ZeXKlezatcu6bvPmzdbHTVu04uCyGXgEheDs4YPJ2ZnEfWvxDMoez+UM4EhcEheS1PWiMFLyQURERERECkRgYCCfffYZXbp0sU436ePjg7+/P0uWLOG7776zDog4cOBAkpOT7RLnwIEDSU1NJSoqyjqF5u7duwFYvHgxjz76KACnTp2iVatW1KlTh+DgYH799Ve++OKLfJ8vODiYHj16UKtWLRo0aGBtTQGwe/dumjRpQkhICHWrVaNP69bUqVPHstHNDfr0gcsG2cyNn58fe/bsISQkhKioKObOnUuRIkVyLDtnzhwOHjxI7dq1CQ4OpmvXrpw7d866PSoqik8//TTXpEqVKlWYO3cugwcPJiQkhBo1ajBx4kTr9kZNW5KZcBqPf5MNHhVCMdJTcA2seM3rALiUmr9xO8QxmIysdjfi0BISEvDz8yM+Ph5fX197h+OQzGYzp0+fJjAw0NosTW5vqjPHpHpzPKozx6M6c0yFpd4uXrxo/dC7cOFCRo0axd69e+0c1c1RYHV25gwsXAjHj4OrKwwbBn5+ed49NjaW0NBQm1Ya9hSXmEa9sSuue/8dL99LMW+3AozoP4XldXa7yM/nUN1tERERkZvkxRdfJDg42PrN6uWj2OenbFJSEj179qRKlSpUrVqVBQsWWLe1bNmShQsX3nCsUVFR1KlTh9DQUBo0aMCqVaus266cstAwDOIS0/g7Lom4xDT0XZZcbtKkSYSEhFC7dm3effdd5syZY++Qbl9mM/z+O0ydakk8eHhYZrJw8C8Ti3m5UqG4F/kdtcEEVCjuRVEv12uWFcejASdFREREbpLnnnuON998E4Djx49To0YNIiMj8ff3z1fZ9957D3d3dw4ePMjhw4dp2LAhrVq1okSJEgUW6wcffEDRf5t279ixg9atW3P27Fmbbwbjk9P5dtsxPl8fy5G4JOv6CsW96BcRRLewsvh56kPDnW706NGMHj3a3mHc/k6dgkWL4MQJy3LVqtCx43UlHoKCgm6bVg9gGeiyX0QQY5fEXLvwFfo30WCThZVaPoiIiMgdYfHixdSoUYM6derw/PPP4+/vz7Rp02jTpg1gaTrq6urKtGnTAMtI9gMGDADg5MmTPPzww4SHhxMcHMxLL71kPW5QUBBjxoyhcePGVKxYkTfeeMO6rehl/bQvXbqEYRiYzeYc47ta2Xnz5vHYY48BULFiRVq2bMn3339vLb9q1SoaNGhAlSpVeOaZZ3JtiTBhwgQaNGhgbd2wYcOGHM8fHx+fbd+3J/0fJSvVZHCnJuz+6UubbUfjkhi7JIbG41bx659ncjy3iFwmKQmmT7ckHjw84IEHoGdPh2/xcLluYWXxdHO+ckbQXDmZwNPNma71yt7cwMRulHwQERGRQu/06dNERUXx7bffsmvXLqpUqcK5c+do1qwZW7duJTU1ldWrV9OgQQNWrlwJwIoVK4iMjASgX79+DB06lM2bN7Njxw62bt3K/Pnzrce/cOECGzZsYMuWLYwfP57jx49bt3300UdUq1aNevXqMW3aNAIDA3ONM7eyR48epUKFCtZyQUFBHD161LocExPD+vXr2bVrF7/++itfffVVjsfv06cPW7ZsITo6mkmTJmUbTG7kyJFUrlyZrl278u2331pbPaRkmFm6aS939fuAkn3eI2HbElKO/deH3/j3Jzk9k6iZm5WAELkWLy+IiIBq1WDoUAgJIc+f0h2En6crn/QOw8S1Ly1r+9TeYWo9VYgp+SAiIiKF3saNG6lTpw41a9YELCPdu7m54enpSWhoKOvWrWPlypWMHDmS7du3Yzab+eWXX7jnnntITExk1apVPPnkk4SGhlK/fn0OHjxonUoQoFevXgD4+/tTqVIlDh8+bN02fPhw9u/fz/r163nrrbdsRpS/Un7KXq5v3764urri5eVF7969bcZruNyOHTto0aIFtWvX5rHHHmP//v02sw28/fbbHDp0iG+++Ybnn3+etLQ04pPTibuUik9IG8CEs5cfXlUbk3IkOtvxDcOShHh89jbikzVVnohVZiasWQMnT/63rnlz6NHDMp1mIdWiagAzo8LxdHW2JCGu2J61ztPVmVlR4TSvGnDrg5RbRskHERERueNc3p84MjKSlStX8ttvv9G6dWuCg4OZPXs2xYoVo2TJktYuDBs3biQ6Opro6GgOHjxo0/XCw8PD+tjZ2ZmMjOzTxIWEhFCmTBnWrFlDTEyMdWDJoUOHXrUsQPny5Tly5Ih1e2xsrM1UfTld3/79+6lXr571HGlpaXTt2pX33nuPPXv28NtvvwGQmpqabf/IyEguXrzI7t27+XbbMcyGJbGQF4YByWmZfLf9WN52ECns/vkHpk2zJB8WLrQMMgng5FToWjvkpEXVADaMas2YTjUpX9zLZlv54l6M6VSTjaNbK/FwB9CAkyIiIlLoNW7cmKioKPbt20f16tWZMWMGaWlpgOWD9sMPP0yFChXw9vYmMjKSMWPG0KVLFwB8fHxo1aoVb7/9Nq+++ioAJ06cwGw2U7bs1fsmx8TEWFtbHDp0iB07dlCzZk1q1KhhM3vE1coCPPTQQ0ydOpVGjRpx+PBh1qxZw5QpU6z7zp49m169epGRkcHcuXN58sknqVatGtu3b7d2nUhISCAtLc2atJg0aZJ1//T0dI4cOUKVKlUA2Lx5M6dPn6ZixYqM+GwnAJd2r8KjfDCZyRdJOrAB/07PX/XaZ62LpX+EBo6TO1hGBvz2G6xda0k4eHlB06Z3RMLhSn6erkQ1qUj/iCAuJKVzKTUDH3cXinq56j3iDqLkg4iIiBR6AQEBzJgxgwceeAA3NzfatWtnnSmifv36xMfH07p1awDuvfdehg0bZl0GmDNnDiNGjKB27dqYTCa8vb359NNPr5l8eP755zl8+DCurq64uLjw8ccfU6NGjXyXfe655xgwYACVK1fG2dmZjz/+2GbGjBo1atCkSRPi4uK4//776dGjB2fO2I674OvryxtvvEF4eDj+/v706NHDui09PZ1+/foRHx+Pi4sL3t7eluk83X2ss1o4e/nyz6wnMacmUaReRzzK5nwdYOl6cSQuiQtJ6RTzdrvqPRIplE6csLRyOH3aslyrFnToAN7edg3L3kwmE8W83fS+cIcyGZqYOZvJkyczfvx4Tp48SUhICJMmTSI8PDzX8hMnTuSTTz7h6NGj+Pv78+CDDzJu3DhrE8xx48bx3XffsW/fPjw9PYmIiOCdd96hWrVq1mO0bNmSX3/91ea4gwcPZurUqXmKOSEhAT8/P+Lj4/EtRKPk3kpms5nTp08TGBhoM62Y3L5UZ45J9eZ4Cmud+fv7s3XrVoKCguwdSoErqDr7Oy6JZu+uvu79f3++FeWuaGYtuSusr7XCLMc6+/tvmDnT0trB29uSdKhVy76BipVeZwUrP59D1fLhCvPmzWPEiBFMnTqVhg0bMnHiRNq2bcv+/ftzHJ167ty5jBw5khkzZhAREcGff/5J//79MZlMTJgwAYBff/2VoUOH0qBBAzIyMhg9ejRt2rQhJiYG78uyn4MGDeL111+3Lnt56Y+1iIhITgzD4HxSOompGXi7u1BMTXdvCm/3G/tX0ecG9xdxSGXLWn58faF9+zu+tYNIFqV6rjBhwgQGDRpEVFQUNWvWZOrUqXh5eTFjxowcy69fv54mTZrQq1cvgoKCaNOmDT179mTz5s3WMsuWLaN///7UqlWLkJAQZs2axdGjR9m2bZvNsby8vChZsqT1Ry0YRERubyaTiQsXLgCWqQ+v7MNvD6+88gomk+m2iCUvTpw4QbNmza5ZLuv+xienM2PtYVqOX0O9sSto9u5q6o1dQcvxa5ix9nC+Zlg4e/ZsoWz1UJCKeblSobhXthHqr8UEVCjuRVEvTZknd4D0dPj9d/h3HBlMJujdGx58UIkHkcsoHX2ZtLQ0tm3bxqhRo6zrnJyciIyMZMOGDTnuExERwezZs9m8eTPh4eH89ddf/Pjjj/Tp0yfX88THxwNQvHhxm/Vz5sxh9uzZlCxZkk6dOvHyyy/n2vohNTXVZnTqhIQEwNKMyJw1gq7ki9lsxjAM3T8HojpzTIWt3i5/37X3e/DmzZvZvHkzFSpUKNBYbladZWRkULJkSX799dc8HXtrbBy9v1tJclomYPsNyrG4RN5c8gcTft7Hx4+E0fxu/5wPcocoyDrr17gCby6NIT/9dE1A/4gKGIaBevjmXWF7f7wTmI8cwXPuXEhNxZyYCO3aWTa4uPw3q4XcVvQ6K1j5uY9KPlzm7NmzZGZmctddd9msv+uuu9i3b1+O+/Tq1YuzZ8/StGlTDMMgIyODxx57jNGjR+dY3mw289RTT9GkSRNq165tc5wKFSpQunRpdu3axQsvvMD+/fv57rvvcjzOuHHjeO2117KtP3PmDCkpKXm9ZLmM2WwmPj4ewzDU/8tBqM4cU2GrtzNnzpCWlkZmZiZxcXGcPn2aqVOnsnDhQjIyMnBxceGNN96gfv36ADRo0IBu3bqxbt06Tpw4wfDhw3F3d2f27NmcOnWKF1980TrLwpAhQzh06BDp6emULl2aCRMm5NgFECApKYnHH3+czz77jC5dulhjyUmpUqV44YUX+Pnnnzlz5gyvv/46Bw4cYOnSpVy8eJH33nuPiIgIMjIy6N27N+fPnycxMZHg4GDef/99vLy86N69O3369KFjx46ApSXgK6+8wooVK7h06RKvvPIKMTExpKamUq9ePd566y3c3Nzo2rUrNWvWJDo6Gg8PDz744AMiIyPZv38/AFu3buX1118nMTERwzB4/vnnadeuHSlp6XyxejdBpStxOHoJ/+z6jXp9XsTdx8/m2kxk8P7CTWREVqV2Gb9s136nKMjXWYvybiwJMJGWYc5TAsIJcHVxonk5t1yfg5Kzwvb+WKilp+O2di0u27aRlpzMxRIlSPP1JVPP+dueXmcF6+LFi3kuq+TDDVqzZg1vvfUWU6ZMoWHDhhw8eJAnn3ySsWPH8vLLL2crP3ToUPbs2cPatWtt1v/vf/+zPg4ODqZUqVK0bt2aQ4cOUbly5WzHGTVqFCNGjLAuJyQkUK5cOQICAtRd4zqZzWZMJhMBAQF6I3IQqjPHVNjqLSAggKJFi+Ls7Ezx4sUJDAzk8ccfZ8yYMQBs3LiRAQMGEBMTA4CzszMAmzZt4uDBg4SEhDB69Gg2b97Mli1b6Nixo/VvwieffEJAgGXe83feeYfJkyfzySef5BjHE088wbBhw6hbt65NLLkpWbIkW7duZdWqVTzwwAN89NFH7Nixg/nz5zNu3Dg2bdqEYRjMnz+fYsWKcfr0aV577TXmzZvHCy+8wKBBg/jqq68YMGAAAAsXLmTQoEEEBgby8ssvc++99/Lll19iGAb/+9//+Prrr3n22Wdxc3Pj77//Zt26dbi6uhIbG4vJZCIwMJC4uDgGDhzIN998Q7NmzTCbzVy4cAFnzyLEJWXwV4LBxfXTyYg/RUC3N/gr3R3OZ782kwmeWvQXv79wD36ed2az/4J+nQ3v6MajszZjAFdryGAyWVo9TO/fgIrl7uzWJ9ejsL0/FlpHjsDixZji4jC8vMioWRPvhx6iiLpYOAS9zgpW1iQLeaHkw2X8/f1xdnbm1KlTNutPnTpFyZIlc9zn5Zdfpk+fPjz66KOAJXGQmJjI//73P1588UWbJ/SwYcNYsmQJv/322zWn5mrYsCEABw8ezDH54O7ujru7e7b1Tk5OehHdAJPJpHvoYFRnjqkw1dvl15H1eOfOnbz55pucO3cOFxcX9u/fT2pqKp6engD06NEDJycnqlatioeHBw899BBOTk6Eh4cTFxdHQkICRYsW5euvv+bLL78kJSWFlJQU/P39c7xnK1as4OjRo0yePDnHuHKSFUN4eDiJiYn06tULJycnGjVqxIEDB3BycsJsNvPhhx+ydOlSUlJSSExMJCIiAicnJ7p168ZTTz3FqVOnKFKkCEuXLuWDDz7AycmJRYsWsXHjRiZOnAhAcnIyLi4u1nj69Olj/Rt2+b3btGkT1apVo0WLFtZ1/v7+zFh7GLMBZ5ZNxi2wEv4PjAaTE7k29DQgMc3MwugTRDWpmOe6LGwK8nXWslog06Ma8vjsbdZuL5fnILLGhPBwdWZq7zCaVw244XPeqQrT+2OhtGMHLF5sycL5+mLcdx9pRYvi5O2tOnMgep0VnPzcQyUfLuPm5kZYWBirVq2yNnk1m82sWrWKYcOG5bhPUlJSthue9a1WVh9HwzB44okn+P7771mzZg0VK177H6GsgcJKlSp1nVcjIiL2kJaWRteuXVm9ejUNGjSwTkF1efLh8m8JnJ2drcsmkwmTyURGRgZr167lo48+YsOGDQQGBrJ48WJra4q3336br7/+GrC0iFizZg3bt2+3Dp547NgxOnTowKeffsr58+etsy89+eSTREVF2cSQ9Tfr8uWMjAzAMqPTL7/8wurVq0lJSWHevHmsXm2ZdtHT05OHHnqIL7/8koCAAO655x5KlCgBWP7uffvtt1StWjXHe+Tj45Pn+2kYBp+vjwXAvVxtUmKjybx0HpciJa6576x1sfSPCNIsGAWkRdUANoxqzXfbjzFrXSxH4pKs28oX96J/kyC6hZXF1+PObG0id4jKlcHdHWrUgLZtwc0N1NVCJE+UfLjCiBEj6NevH/Xr1yc8PJyJEyeSmJho/Wetb9++lClThnHjxgHQqVMnJkyYQN26da3dLl5++WU6depk/Ydu6NChzJ07l0WLFlGkSBFOnjwJgJ+fH56enhw6dIi5c+fSoUMHSpQowa5du3j66adp3rw5derUsc+NEBGR65KSkkJaWhrly5cHYNKkSdd1nPPnz1OkSBFKlChBWloan376qXXbyJEjGTlypHW5bdu21r9LYJkZYuHChYSGhgKWv13XG4O/vz++vr6cPXuWzz//3HpdAFFRUfTr14/AwECbeLp06cI777zDp59+iouLC+fPn+fcuXNUqVLlqueLiIjgwIED/P7779ZuF4ePn7Z+yPWpdQ/upatz6usXCXzoVVyL5twqESzfyh+JS+JCUjrFvN2u6/olOz9PV6KaVKR/RBAXktK5lJqBj7sLRTXVqRRWaWmwdy+EhFiWfX1h2DDISqJq0EKRPFPy4Qrdu3fnzJkzjBkzhpMnTxIaGsqyZcusg1AePXrUpqXDSy+9hMlk4qWXXuL48eMEBATQqVMn3nzzTWuZrP65LVu2tDnXzJkz6d+/P25ubqxcudKa6ChXrhzdunXjpZdeuvkXLCIiBcrX15c33niD8PBw/P396dGjx3Udp127dsyePZtq1apRokQJIiMjOX78eAFHe3V9+/Zl0aJF1KhRAz8/P5o2bcrRo0et28PDw3F2dubgwYO0adPGuv6DDz5g5MiRhIaG4uTkhIuLC+++++41kw/FihXj+++/55lnnuHixYs4OTkx/PmXAE9rGe/qTTG5unP6m5cJ7DYG1xLlrnrMS6kZSj7cBCaTiWLebrq3UrgdPgyLFsGFC+DpCVmtufLRektE/mMyNP9RoZDVrDc+Pl4DTl4ns9nM6dOnCQwMVP8vB6E6c0yqN8djzzqLS0yj3tgV173/jpfvvSM/IOt15phUb7eJ1FRYsQK2brUs+/nBAw/Av13bLqc6czyqs4KVn8+havkgIiIit61iXq5UKO7F0bikPE3zmMWEZRyCol4af0BE8uGvvyytHeLjLcv168O991rGeRCRG6JUj4iIiNy2TCYT/SKCrmvf/k002KSI5MOqVfDFF5bEQ9Gi0K8fdOyoxINIAVHyQURERG5r3cLK4unmTF7zCE4m8HRzpmu9q09rLSJio0wZy+/wcBgyBPIwQ52I5J2SDyIiInJb8/N05ZPeYZjgmgmIrO1Te4fh56kuFyJyFSkp8Pff/y1Xrw5Dh0KHDpYpNEWkQCn5ICIiIre9FlUDmBkVjqersyUJccX2rHWers7MigqnedWAWx+kiDiOP/+EyZNh7ly4dOm/9QF67xC5WTTgpIiIiDiEFlUD2DCqNd9tP8asdbEciUuybitf3Iv+TYLoFlYWXw+1eBCRXCQnw7JlsHOnZblECUhM1PSZIreAkg8iIiLiMPw8XYlqUpH+EUFcSErnUmoGPu4uFPVy1eCSInJ1+/fDkiVw8aKlj1bjxtCqFbgqYSlyKyj5ICIiIg7HZDJRzNuNYt7qly0i12AY8P33sGuXZdnfH+6/H8qVs29cIncYJR9ERERERKTwMpnAw8PyOyICWrZUawcRO1DyQURERERECpekJEhPBz8/y3JkJISE/Ded5nUyDIPzSekkpmbg7e6Cn4dzAQQrcmdQ8kFERERERG6JiRMn0qNHD0qWLHnVcsnJyYSFheHm5kZ0dHT+ThITA0uXWmau6NfP0uLBzS3XxMPUqVO5ePEizz33XK6HjE9O5/05PzJ10kS82j9rXR9U3JOB9UvQsUgxinm75y9OkTuMptoUEREREZFbYuLEiZw8efKa5V544QWaNGmSv4MnJsL8+fDNN5bHSUmW39fw2GOPXTXx8OufZ2g8bhVfHnTB+7LEA8DfcUnM23KUZu/8wq9/nslfvIXQiy++SHBwMKGhoYSGhvL111/nWvbAgQO0atWK0NBQqlevzjPPPIPZbAagf//+TJw40VrWMAziEtP4Oy6JuMQ0DMO42ZciN4GSDyIiIiIikm+LFi2iRo0ahISE8MILL+Dv709sbCxBQUE899xzhIWFUaVKFcaPHw/A66+/zokTJ+jevTuhoaG5tmhYuXIlx48f55FHHrFZn5mZyXPPPUft2rWpXbs2TzzxBGlpaZYBJffsgcmT4Y8/wMmJWcnJRH77LT0HDaJmzZpEREQQExPDAw88QI0aNWjTpg2XLl0C4NVXX+Wpp54CYNasWURGRtKzZ0+Cg4OpVjuU3h8sJjk9k+Sjuzg+8wmbmIx/f5LTM4maufmOT0A899xz7N69m+joaJYuXcr//vc/zp49m2vZBx54gOjoaKKjo/n5559ZtmyZTZn45HRmrD1My/FrqDd2Bc3eXU29sStoOX4NM9YeJj45/VZclhQQJR9ERERERCRfTp8+zYABA/j+++/ZuXMn1atX59y5c9btp06dYuvWrWzcuJFJkyaxfv16xowZQ+nSpZk3bx7R0dGEhoZmO+6FCxd4/vnn+eSTT7JtmzZtGlu2bGHbtm1ER0dz6NAhPnj3XUtLhwULLC0dSpaEQYOgRg22bN3KO++8Q0xMDJUrV6ZTp05MnTqVvXv34ubmxueff57jtW3ZsoW33nqLtZu3c86vKhc2LuBaX7QbhiUJ8fjsbbfNB+LFixdTo0YN6tSpw/PPP4+/vz/Tpk2jTZs2ACQkJODq6sq0adMA+OKLLxgwYAAAJ0+e5OGHHyY8PJzg4GBeeukl63GDgoIYM2YMjRs3pmLFirzxxhvWbUWLFrU+vnTpEoZhWFszXMlkMhEfHw9Yutmkp6dTqlQp6/aff99MqaohDO7clG1fvoE5PdW67WhcEmOXxNB43Ko7PuHjSJR8EBERERGRfNm4cSN16tShevXqAPTr1w83t/+mvh04cCAmkwl/f3+6du3KypUr83TcYcOGMXr0aAIDA7NtW7lyJf3798fd3R0XFxcGDRrEitWr4dw5cHKyzGIxaBD8+wG2cePGlC9fHoD69evToEED7rrrLgAaNGjAgQMHcowh60P1t9uO4VyyGhnn/8lT7IYByWmZfLf9WJ7K30ynT58mKiqKb7/9ll27dlGlShXOnTtHs2bN2Lp1K6mpqaxevZoGDRpY62bFihVERkYClvocOnQomzdvZseOHWzdupX58+dbj3/hwgU2bNjAli1bGD9+PMePH7du++ijj6hWrRr16tVj2rRpOdYlWLrgzJ8/n9KlS1O6dGn69u1L3bp1ATiZkMLK39YR8NBrlH70E8zJF7m4dZF1X7U4cUxKPoiIiIiIyE1lMplyXB8REUFoaCgNGzYEYO3atTz77LMEBQXRo0cPYmJiqFatmu1Oly5BZqblmCYTdO3Kg5s3E/rUU4SGhVlbYHh4eFh3cXZ2zrackZGRY0weHh4YhsHn62PB5IRhzszXtc5aF2v3MQmykkM1a9YELMkgNzc3PD09CQ0NZd26daxcuZKRI0eyfft2zGYzv/zyC/fccw+JiYmsWrWKJ598ktDQUOrXr8/BgwfZv3+/9fi9evUCwN/fn0qVKnH48GHrtuHDh7N//37Wr1/PW2+9ZdMi5nJTpkyhZ8+enDhxgiNHjjBnzhxWrFhBfHI6Gw+dw7t6U0xuXpicnPGp04bk2Ohsx7gdW5xI7jTbhYiIiIiI5EujRo3YtWsX+/fvp1q1asyePdsy/sK/Zs2aRYsWLYiLi+P777/nq6++AsDX19fa1B5g/fr1NseNjY21Pl6zZg1PPfWUdWyIyNat+WLKFHodO4ZTkyZ89tlnli4EJUuyYMmSAr2+80npHIlLIr8pBAM4EpfEhaR0inm7XbP8rXJ58icyMpKVK1fy22+/8fbbbxMcHMzs2bMpVqwYJUuWtI6FsXHjRpuEzeXyksgJCQmhTJkyrFmzhho1algTFk2aNGHy5MlMnjyZP//8E4DAwEA6dOjAmjVr+NuzChlmA5crbn4u+SubFidRTSrm+Z7IraeWDyIiIiIiki+BgYF89tlndOnShdDQUHbv3o2Pj4+1z39AQABhYWGEh4czbNgwIiIiAMu34oMGDbrqgJM5Skjgf76+1HNxod5HHxHauzdBFSpYB4osaImpObeKyKtLN7j/jWrcuDG7du1i3759AMyYMcOaHIqMjGTu3LkULVoUb29vIiMjGTNmjLXLhY+PD61ateLtt9+2Hu/EiRMcO3bt7iQxMTHWx4cOHWLHjh3UrFmTmjVrWgeWnDx5MgCVKlWyDjCZmJjI6tWrqVWrlqXFCZC0fx3mtGQMcyaXdq/Eo0LoVc99O7Q4kaszGaqhQiEhIQE/Pz/i4+Px9fW1dzgOyWw2c/r0aQIDA3FyUl7OEajOHJPqzfGozhyP6swxOVq9Xbx4kSJFigCwcOFCRo0axd69ewkKCmLhwoU5DiiZb4YB0dGwfDmkpICzs2VshyZNLOM83CRxiWnUG7vimuWcMKhRzGDveRNm/vtqfsfL99q95cOiRYsYOXIkbm5utGvXjunTp7N161bKly9PiRIlePrppxkzZgx//vkn1apVY/HixXTq1AmwjBkxYsQIoqOjMZlMeHt78+mnnxISEpKtfuvXr897771Hy5Yt6dixI4cPH8bV1RUXFxdeeOEFHnrooRzj27FjB8OGDePSpUukpaXRuXNnnn/5dcLeWMnZpR+AyURG3HEyk+JxL1Od4m2G4uTqftVrzst9d7TX2e0uP59D1e1CRERERETybdKkScybN4/MzEx8fX2ZM2dOwZ4gIQEWL4aDBy3LZcpAly4QEFCw58lBMS9XKhT34mg+u16YgPLFvSjq5XqzQsuz+++/n/vvv9+6PH36dACcnJw4f/68dX3VqlWztRgIDAxk9uzZOR738q4xAFu3brU+XpKP7i9169Zl3bp1Nuv+jksCwP++p/N8nMtdSs2we9JHcqfkg4iIiIiI5Nvo0aMZPXp0tvVXfji9bmlpEBsLLi7QqhU0bnxTWztczmQy0S8iiLFLYq5d+Ar9mwTlOsCmXJ23+419PPW5wf3l5lLtiIiIiIjI7SE1Fdz/bVrv729p6VCypOXxLdYtrCzv/byf5PRM8tJR3ckEHq7OdK1X9uYHdx3Onj1r7xCuqTC0OJHcqZOLiIiIiIjYl2HA1q3wwQfw99//ra9d2y6JBwA/T1c+6R2GidxnWsiStX1q7zD8PPUB+HpltTi5HmpxcvtT8kFEREREROzn/Hn44gtYssQyqOS2bfaOyKpF1QBmRoXj6epsSUJcsT1rnaerM7Oiwmle9eaPR1HYdQsri6eb8zUTPlmcTODpdvu2OJH/qNuFiIiIiIjceoYBW7bAypWW8R1cXaF1awgPt3dkNlpUDWDDqNZ8t/0Ys9bFcuTfQREByhX3okf9EnRsVJ2iXlefiUHyJqvFSdTMzWDiql1e1OLEsSj5ICIiIiIit1ZcnGUmi6zBKStUgPvvh+LF7RpWbvw8XYlqUpH+EUFcSErnUmoGPu4u+Ho4c+bMGXw99MG3IGW1OHl89jaS0zIBbMaAyGoU4enqzNTeYWpx4iCUfBARERERkVsrNtby4+YGkZHQoMG1B1a4DZhMJop5u1mnczSbzXaOqPC6WouT8sW96N8kiG5hZZX4cSBKPoiIiIiIyM2XmQnOzpbHdetaxnqoVw+KFbNvXHLbyq3FSVEvVw0u6YCUfBARERERkZvHbIZNmyyzWQwaBB4ellYOrVvbOzJxEFe2OBHHpOSDiIiIiIjcHGfPwqJF/02fuWMHNG5s35hExC6UfBARERERkYJlNsOGDbB6NWRkgLs7tG1r6W4hInckJR9ERERERKTgnDljae1w7JhluUoV6NQJ/PzsG5eI2JWSDyIiIiIiUnB+/92SePDwsLR2CA11iJksROTmUvJBRERERERujGH8l2Bo29byOzISfH3tF5OI3Fac7B2AiIiIiIg4qMxM+O03+P77/9Z5e0PXrko8iIgNtXwQEREREZH8O3nSMrbDP/9YlsPCoEIF+8YkIrctJR9ERERERCTvMjMt4zr89ptlVgtPT+jQAcqXt3dkInIbU/JBRERERETy5p9/YOFCOHXKslyjBtx3H/j42DUsEbn9KfkgIiIiIiLXZjbD/PkQFwdeXpbWDrVqaSYLEckTJR9EREREROTanJwsrRy2bbP89va2d0Qi4kCUfBARERERkewyMuDXX6FYMahXz7KucmXLj4hIPin5ICIiIiIito4ft4ztcOYMuLtD9eqWrhYiItdJyQcREREREbHIyIDVq2H9ejAMy0CS992nxIOI3DAlH0REREREBP7+GxYtgrNnLct16kC7dko8iEiBUPJBREREROROl5AAs2ZBZqaltUOnTlCtmr2jEpFCRMkHEREREZE7na8vNGoEly5ZWjt4eto7IhEpZJR8EBERERG506SlWcZ2qFcPAgIs6yIjwWSyb1wiUmgp+SAiIiIicic5csQytkNcnGWch4EDLUkHJR5E5CZS8kFERERE5E6QlgYrV8LmzZZlX19o2VJJBxG5JZR8EBEREREp7A4fhsWL4fx5y3JYGNx7L3h42DcuEbljKPkgIiIiIlKYHTwIs2dbHvv5QefOULmyfWMSkTuOkg8iIiIiIoVZpUpQpgyUKmVp7eDubu+IROQOpOSDiIiIiEhhkpoK69dDs2bg4gJOThAVZXksImInegcSERERESksDh6EH36A+Hgwm6F1a8t6JR5ExM70LiQiIiIi4uhSUuDnn2H7dstysWIa10FEbitKPoiIiIiIOLIDByytHRISLNNmNmwI99wDbm72jkxExErJBxERERERR7VhAyxfbnlcogTcfz+UL2/fmEREcqDkg4iIiIiIo6pRA9asgXr1LK0dXF3tHZGISI6UfBARERERcRRJSfDnnxAaalkuWhSefBK8vOwZlYjINSn5ICIiIiLiCPbuhaVL4dIl8PODihUt65V4EBEHoOSDiIiIiMjtLCkJfvwR9uyxLAcEgLu7fWMSEcknJ3sHcDuaPHkyQUFBeHh40LBhQzZv3nzV8hMnTqRatWp4enpSrlw5nn76aVJSUvJ1zJSUFIYOHUqJEiXw8fGhW7dunDp1qsCvTUREREQcSEwMTJ5sSTyYTNCsGQweDKVL2zsyEZF8UfLhCvPmzWPEiBG88sorbN++nZCQENq2bcvp06dzLD937lxGjhzJK6+8wt69e5k+fTrz5s1j9OjR+Trm008/zQ8//MD8+fP59ddfOXHiBF27dr3p1ysiIiIit6klS+CbbyAxEQIDYdAgaN0aXNR4WUQcj5IPV5gwYQKDBg0iKiqKmjVrMnXqVLy8vJgxY0aO5devX0+TJk3o1asXQUFBtGnThp49e9q0bLjWMePj45k+fToTJkzgnnvuISwsjJkzZ7J+/Xo2btx4S65bRERERG4zFSqAkxM0bw7/+59aO4iIQ1Pa9DJpaWls27aNUaNGWdc5OTkRGRnJhg0bctwnIiKC2bNns3nzZsLDw/nrr7/48ccf6dOnT56PuW3bNtLT04mMjLSWqV69OuXLl2fDhg00atQo23lTU1NJTU21LickJABgNpsxm803cBfuXGazGcMwdP8ciOrMManeHI/qzPGozhyTOSEB07FjmP39LStq1oRSpaB48X8LqD5vN3qtOR7VWcHKz31U8uEyZ8+eJTMzk7vuustm/V133cW+ffty3KdXr16cPXuWpk2bYhgGGRkZPPbYY9ZuF3k55smTJ3Fzc6No0aLZypw8eTLH844bN47XXnst2/ozZ85kG29C8sZsNhMfH49hGDg5qVGQI1CdOSbVm+NRnTke1ZmDMQxc9u7FbdUqSE3lTLFimLy9/9ueS/dfsT+91hyP6qxgXbx4Mc9llXy4QWvWrOGtt95iypQpNGzYkIMHD/Lkk08yduxYXn755Zt23lGjRjFixAjrckJCAuXKlSMgIABfX9+bdt7CzGw2YzKZCAgI0BuRg1CdOSbVm+NRnTke1ZkDuXgRlizB9OefGM7OGAEBePv44BQQYO/IJA/0WnM8qrOC5eHhkeeySj5cxt/fH2dn52yzTJw6dYqSJUvmuM/LL79Mnz59ePTRRwEIDg4mMTGR//3vf7z44ot5OmbJkiVJS0vjwoULNq0frnZed3d33HOYYsnJyUkvohtgMpl0Dx2M6swxqd4cj+rM8ajObnOGAbt2wU8/QUoKuLhgNGtG8t13U0QfihyKXmuOR3VWcPJzD3W3L+Pm5kZYWBirVq2yrjObzaxatYrGjRvnuE9SUlK2G+7s7AyAYRh5OmZYWBiurq42Zfbv38/Ro0dzPa+IiIiIOKjMTPjqK/j+e0vioXRpy4CSzZvDv/9HiogUNmr5cIURI0bQr18/6tevT3h4OBMnTiQxMZGoqCgA+vbtS5kyZRg3bhwAnTp1YsKECdStW9fa7eLll1+mU6dO1iTEtY7p5+fHwIEDGTFiBMWLF8fX15cnnniCxo0b5zjYpIiIiIg4MGdn8Pa2/G7ZEpo0scxqoQHwRKQQU/LhCt27d+fMmTOMGTOGkydPEhoayrJly6wDRh49etSmpcNLL72EyWTipZde4vjx4wQEBNCpUyfefPPNPB8T4IMPPsDJyYlu3bqRmppK27ZtmTJlyq27cBERERG5eeLjLQmGIkUsy23bQuPGEBho37hERG4Rk2EYhr2DkBuXkJCAn58f8fHxGnDyOpnNZk6fPk1gYKD6fzkI1ZljUr05HtWZ41Gd3UYMA3bsgOXLoXx56NULTKYci6reHI/qzPGozgpWfj6HquWDiIiIiMjNcOEC/PADHDpkWU5OhtRUyMfo8CIihYWSDyIiIiIiBckwYNs2+PlnSEsDFxdo3RoaNrR0vRARuQMp+SAiIiIiUlAuXYJvv4XDhy3L5cvD/fdDiRL2jUtExM6UfBARERERKSju7pCQAK6ultYO4eFq7SAigpIPIiIiIiI35sIF8PW1JBlcXeHBBy1JiOLF7R2ZiMhtQ2lYEREREZHrYRiwcSNMngybNv23vlQpJR5ERK6glg8iIiIiIvl17hwsWgRHj1qW//oLGjXKdRpNEZE7nZIPIiIiIiJ5ZTZbWjmsWgUZGeDmBvfeC/XrK/EgInIVSj6IiIiIiOTFuXOwcCH8/bdluVIl6NwZiha1Z1QiIg5ByQcRERERkbzIyIDjxy2DSbZpA/XqqbWDiEgeKfkgIiIiIpKb5GTw9LQ8vusueOABKF8e/PzsG5eIiIPRbBciIiIiIlcym2HtWvjgA/jnn//WBwcr8SAich3U8kFERERE5HKnT1vGdjhxwrK8a5dl+kwREbluSj6IiIiIiABkZsK6dfDrr5bHHh7Qvj3UqWPvyEREHJ6SDyIiIiIip05ZWjtkdbGoWhU6dYIiRewalohIYaHkg4iIiIhIbKwl8eDpaWntEBysmSxERAqQkg8iIiIicmfKyACXf/8dDg+HxETLbx8f+8YlIlIIabYLEREREbmzZGbC6tUwdSqkpVnWmUxwzz1KPIiI3CRq+SAiIiIid44TJ2DRIssYDwB//AF169o3JhGRO4CSDyIiIiJS+GVkWGaxWLcOzGbw8oL77oNatewdmYjIHUHJBxEREREp3I4ft8xkceaMZbl2bcugkt7edg1LROROouSDiIiIiBRuv/9uSTx4e0PHjlCjhr0jEhG54yj5ICIiIiKFj2H8N1XmffdZEg+tW1u6W4iIyC2n5IOIiIiIFB7p6ZaZLJKSoEsXy7oiRaBTJ7uGJSJyp1PyQUREREQKh6NHLTNZnDtnWW7YEEqVsm9MIiICKPkgIiIiIo4uPR1WrYJNmyzdLbJaOijxICJy21DyQUREREQc15EjltYOcXGW5bp1oW1b8PCwb1wiImJDyQcRERERcUwZGbBgAVy8CL6+0LkzVKli76hERCQHSj6IiIiIiGNycbFMnbl/P7Rpo9YOIiK3MSUfRERERMQxpKbCypVQrhzUqWNZV62a5UdERG5rSj6IiIiIyO3vr79g8WK4cAH27LEkHNzd7R2ViIjkkZIPIiIiInL7Sk2FFStg61bLctGicP/9SjyIiDgYJR9ERERE5PZ06JCltUN8vGU5PBwiI8HNzb5xiYhIvin5ICIiIiK3n3PnYPZsMAwoVszS2iEoyN5RiYjIdVLyQURERERuPyVKWFo6mExwzz1q7SAi4uCUfBARERER+0tOtsxk0aQJFC9uWdeunSX5ICIiDk/JBxERERGxr/37YckSuHgR4uKgb19L0kGJBxGRQkPJBxERERGxj+Rk+Okn2LXLslyihKWLhZIOIiKFjpIPIiIiInLr7dtnae1w6ZIl2RARAS1bgqurvSMTEZGbQMkHEREREbm1/vgD5s+3PA4IsMxkUbasfWMSEZGbSskHEREREbm1qleHUqWgcmVLawcX/UsqIlLY6Z1eRERERG6uxETYsAFatQJnZ8vPo49afouIyB1ByQcRERERuTkMA2JiYOlSSEoCNzdo3tyyTYkHEZE7ipIPIiIiIlLwLl2CH3+0JB8A7roL7r7bvjGJiIjdKPkgIiIiIgXHMGDPHssUmklJ4ORkae3QrJlaO4iI3MGUfBARERGRgrNmDfz6q+VxyZLQpYvlt4iI3NGUfBARERGRghMcDBs3QpMmlh+1dhAREcDJ3gHcqL/++sveIYiIiIjcuRISIDr6v2V/f3j6aUtXCyUeRETkXw6ffKhSpQqtWrVi9uzZpKSk2DscERERkTuDYViSDlOmwKJF8Pff/23z8LBbWCIicnty+OTD9u3bqVOnDiNGjKBkyZIMHjyYzZs32zssERERkcIrPh7mzoWFCyElBUqXVsJBRESuyuGTD6GhoXz44YecOHGCGTNm8M8//9C0aVNq167NhAkTOHPmjL1DFBERESkcDAO2b7e0djhwAFxc4N57YeBACAiwd3QiInIbc/jkQxYXFxe6du3K/Pnzeeeddzh48CDPPvss5cqVo2/fvvzzzz/2DlFERETEsS1YAIsXQ2oqlC0Ljz1mGVTSqdD8SykiIjdJoflLsXXrVoYMGUKpUqWYMGECzz77LIcOHWLFihWcOHGC+++/394hioiIiDi2ypUtrR3atoUBAyyDS4qIiOSBw0+1OWHCBGbOnMn+/fvp0KEDX3zxBR06dMDp3wx8xYoVmTVrFkFBQfYNVERERMTRXLgAFy9CuXKW5bp1oVIlKFrUnlGJiIgDcvjkwyeffMKAAQPo378/pUqVyrFMYGAg06dPv8WRiYiIiDgow4CtW2HFCstAkkOGWH6bTEo8iIjIdXH45MOBAweuWcbNzY1+/frdgmhEREREHFxcnGVch9hYy3KpUpCWptksRETkhjh88mHmzJn4+Pjw0EMP2ayfP38+SUlJSjqIiIiI5IVhwObNsHIlpKeDq6tlJosGDSwtHkRERG6Aww84OW7cOPxzGOwoMDCQt956yw4RiYiIiDiY9HSYNQt++snyOCjI0tUiPFyJBxERKRAO3/Lh6NGjVKxYMdv6ChUqcPToUTtEJCIiIuJgXF2hSBFwc7O0dqhfX0kHEREpUA6ffAgMDGTXrl3ZZrPYuXMnJUqUsE9QIiIiIre7s2ct4zj4+FiWO3SAyEgNKCkiIjeFw3e76NmzJ8OHD2f16tVkZmaSmZnJL7/8wpNPPkmPHj2u65iTJ08mKCgIDw8PGjZsyObNm3Mt27JlS0wmU7af++67z1omp+0mk4nx48dbywQFBWXb/vbbb19X/CIiIiK5Mpth/XqYOhWWLrWM9QDg5aXEg4iI3DQO3/Jh7NixxMbG0rp1a1xcLJdjNpvp27fvdY35MG/ePEaMGMHUqVNp2LAhEydOpG3btuzfv5/AwMBs5b/77jvS0tKsy+fOnSMkJMRmAMx//vnHZp+ffvqJgQMH0q1bN5v1r7/+OoMGDbIuFylSJN/xi4iIiOTqzBlYtAiOHbMsp6VBRoal24WIiMhN5PDJBzc3N+bNm8fYsWPZuXMnnp6eBAcHU6FChes63oQJExg0aBBRUVEATJ06laVLlzJjxgxGjhyZrXzx4sVtlr/++mu8vLxskg8lS5a0KbNo0SJatWpFpUqVbNYXKVIkW1kRERGRG5bV2mHNGkuywd0d2rWD0FCN7SAiIreEwycfslStWpWqVave0DHS0tLYtm0bo0aNsq5zcnIiMjKSDRs25OkY06dPp0ePHnh7e+e4/dSpUyxdupTPP/8827a3336bsWPHUr58eXr16sXTTz9tbc1xpdTUVFJTU63LCQkJgKXVh9lszlOsYstsNmMYhu6fA1GdOSbVm+NRnTkemzq7cAHmz8d04gQARpUq0KkT+PpaulxkdbsQu9NrzfGozhyP6qxg5ec+Forkw7Fjx1i8eDFHjx616QIBlpYMeXX27FkyMzO56667bNbfdddd7Nu375r7b968mT179jB9+vRcy3z++ecUKVKErl272qwfPnw49erVo3jx4qxfv55Ro0bxzz//5Br/uHHjeO2117KtP3PmDCkpKdeMVbIzm83Ex8djGAZOTg4/HModQXXmmFRvjkd15nhs6iw9Ha9TpzBlZJDaqhUZtWpBSorlR24req05HtWZ41GdFayLFy/muazDJx9WrVpF586dqVSpEvv27aN27drExsZiGAb16tW7pbFMnz6d4OBgwsPDcy0zY8YMHnnkETw8PGzWjxgxwvq4Tp06uLm5MXjwYMaNG4e7u3u244waNcpmn4SEBMqVK0dAQAC+vr4FcDV3HrPZjMlkIiAgQG9EDkJ15phUb45HdeZ4zGfOYPLzIyAw0FJnAwZAkSJ4azyp25pea45HdeZ4VGcF68rPtVfj8MmHUaNG8eyzz/Laa69RpEgRvv32WwIDA3nkkUdo165dvo7l7++Ps7Mzp06dsll/6tSpa47FkJiYyNdff83rr7+ea5nff/+d/fv3M2/evGvG0rBhQzIyMoiNjaVatWrZtru7u+eYlHByctKL6AaYTCbdQwejOnNMqjfHozpzEJmZsHYt/PorruHhOLVpY6mzsmXtHZnkkV5rjkd15nhUZwUnP/fQ4e/23r176du3LwAuLi4kJyfj4+PD66+/zjvvvJOvY7m5uREWFsaqVaus68xmM6tWraJx48ZX3Xf+/PmkpqbSu3fvXMtMnz6dsLAwQkJCrhlLdHQ0Tk5OOc6wISIiIpLNyZPwf/8Hq1djyszE+d8xHkRERG4HDt/ywdvb2zrOQ6lSpTh06BC1atUCLGM45NeIESPo168f9evXJzw8nIkTJ5KYmGid/aJv376UKVOGcePG2ew3ffp0unTpQokSJXI8bkJCAvPnz+f999/Ptm3Dhg1s2rSJVq1aUaRIETZs2MDTTz9N7969KVasWL6vQURERO4gmZnw22/w+++WWS28vDDatSM1IMDekYmIiFg5fPKhUaNGrF27lho1atChQweeeeYZdv8/e3ceF1W9/3H8NeyLsgmoqAnmriwK4pbmQqktalppaip523frlvYr28t727ytZrlUWlppalaaomkp7uKSaZpbLoiKLIKyzfn9cWKSAAUFh8H38/GYR5xzvufM58yncZgP32XrVubMmUOHDh3Kfb1BgwZx7Ngxxo0bR3JyMlFRUSxcuNA2CeWBAweKdS3ZuXMnv/zyCz/++GOp1505cyaGYXDbbbcVO+bu7s7MmTN57rnnyMnJISwsjEcffbTInA4iIiIixSQnwzffQOGQ0ZYt4brrwMsLUlLsG5uIiMhZLIbh2Osr7dmzh1OnThEREUFWVhaPPfYYq1atokmTJrz55ps0bNjQ3iFeEhkZGfj6+pKenq4JJy+Q1WolJSWF4MLJuaTKU84ck/LmeJSz8jEMg5PZeWTl5OPt7oK/lysWi6VynuzQIfj4Y/D0hOuvh796fypnjkl5czzKmeNRzipWeb6HOnTPh4KCAg4ePEhERARgDsGYOHGinaMSERERR2OxWDh58iR+fn7Fjl133XW89dZbJU4AXei3334jOjqazjcMIjdmOPtTs23HGgZ4MaJTKAOj6+Pr6XrxwWZlgbe3+XO9ejBwIISF/b1PRESkCnLoUo+zszPXXnstJ0+etHcoIiIiUk19//335yw85OXlMej2eFyvbM+aPakcOKvwAHAgNZsXF2yn46sJLP/92IUHkp8PS5bAhAlFh1S0bq3Cg4iIVHkOXXwAaN26NXv27LF3GCIiIlLFWSwWnn76adq0aUPTpk2ZMWNGkePvv/8+sbGxhIWFMXXqVNv+0NBQkpKSSr3uqIfHcMg/EotvXQD+OZ7V+OtxOq+A+KlrL6wAcfAgTJxoLqOZlwe//Vb+a4iIiNiRwxcfXnrpJR5//HEWLFjAkSNHyMjIKPIQERERKWSxWNi0aRMLFy7kwQcfZN++fbZj7u7urF27lh9++IGHHnqI/Pz8814vYflKZi9cSs3oG8/b1jDMIsS90zeQfjqvbAHn5cGPP8LkyXD8ONSoAYMHw9VXl+18ERGRKsKh53wAcxwmQN++fYtM5mQYBhaLhYKCAnuFJiIiIlXMv/71LwAaNWpE165dWbFiBaGhoQAMHToUgObNm+Pi4kJycjL169cv9VrZ2dmMuuse/K99BCjbhJKGAadzC5iz8SDxncPO3fjPP2HuXDhxwtyOjITevc3JJUVERByMwxcfli1bZu8QRERExEGd/YcLDw8P28/Ozs7Fej5s376dIUOGANC5c2fuvvtuDh/6E+sXTwFgzckCw4o15xSB1597uexpK/cxslPouVfB2L/fLDzUrAk33ghNm5b39kRERKoMhy8+XK1uhyIiIlJGU6dO5bnnnmPfvn38/PPPTJgwoczntmzZssjcD6lZuYQ88Pe8EWm/zMB6JouAuLvOeR0D2J+aTVp2Hv7ebkUP5uWB618rYnTqBAUFEBur3g4iIuLwHL74sGLFinMe79q16yWKRERERKq6goIC2rRpQ1ZWFm+//bZtyMWFyMo5/5wQ53IqJ//v4kNuLiQkwN69cNdd4OICTk6a20FERKoNhy8+dOvWrdi+s7swas4HERERKfTYY4/x4osvFttvGEXXqDh+/Ljt57MnpTybt3vRX6P8rhparlhqFJ6/dy/Mnw+FS4f//ju0bFmua4mIiFR1Dl98OFn4Qf2XvLw8Nm3axDPPPMPLL79sp6hERESkuvP3cqVhgBcHUrOLLa95LhbgigAv/FwM+O47WLfOPODrC337wpVXVka4IiIiduXwxQdfX99i+6655hrc3NwYPXo0GzZssENUIiIiUtX8s3fDxbJYLIzoFMqLC7aX+9wHQp2wfPABpKWZO2Ji4JprwN29QmMUERGpKpzsHUBlqV27Njt37rR3GCIiIlKNDYyuj6ebM+datOJsThbwdHXi+sw9ZuHBzw+GD4cbblDhQUREqjWH7/mwZcuWItuGYXDkyBHGjx9PVFSUfYISERGRy4KvpysfDIsmfupasMC5OldYMAALE2+PwSvYFVavhu7dVXQQEZHLgsMXH6KiorBYLMW6Unbo0IEpU6bYKSoRERG5XFzdNIip8bHcO30Dp3PNia7P/q3EPT+Xrns34eLqzG0v3EfXpkHmgd69L32wIiIiduLwxYe9e/cW2XZyciIoKAgPDw87RSQiIiKXm6ubBpE4tidzNh5k2sp97E/NBiA09RC3HE6iU7ArLer54lGr2o54FREROSeHLz40bNjQ3iGIiIiI4OvpSnznMEZ2CiXtRAYFPyzE+7c/8agbhKVWLejXD2rVsneYIiIiduHwxYeHHnqIxo0b89BDDxXZ/+6777J7924mTJhgn8BERETksmT5/Xf8FyyAzExwd4EOHaBHD3B1tXdoIiIiduPwff9mz55N586di+3v1KkTX3/9tR0iEhERkctWTg7Mm2cWHmrVgvh46NVLhQcREbnsOXzPhxMnTuDr61tsv4+PD8ePH7dDRCIiInLZcneH66+HQ4fMlSxUdBAREQGqQc+Hxo0bs3DhwmL7f/jhBxo1amSHiEREROSykZ0Ns2fD9u1/72vVCq69VoUHERGRszh8z4fRo0fzwAMPcOzYMXr06AFAQkICb7zxhuZ7EBERkcqzfTt89x1kZcG+fdC0Kbg4/K9WIiIilcLhPyHvuOMOcnJyePnll3nxxRcBCA0N5YMPPmD48OF2jk5ERESqnaws+P57+PVXczs42FzJQoUHERGRUlWLT8l7772Xe++9l2PHjuHp6UmNGjXsHZKIiIhUR7/+avZ2yM4GJye46iro2lWFBxERkfNw+E/KvXv3kp+fT5MmTQgKCrLt37VrF66uroSGhtovOBEREak+jhyBr74yf65d2+ztEBJi35hEREQchMNPODly5EhWrVpVbP+aNWsYOXLkpQ9IREREqqe6dSEmBq6+Gu66S4UHERGRcnD44sOmTZvo3Llzsf0dOnQgKSnp0gckIiIi1UNmprmSRXr63/uuv95cQtPZ2X5xiYiIOCCHH3ZhsVjIzMwstj89PZ2CggI7RCQiIiIOzTBgyxZYuBBOn4bcXLjtNvOYxWLf2ERERByUw/d86Nq1K6+++mqRQkNBQQGvvvoqV111lR0jExEREYeTmQlffAHffGMWHurWhb+W8nZ0hmGQmpXLn6nZpGblYhiGvUMSEZHLiMP3fPjPf/5D165dadasGV26dAHg559/JiMjg6VLl9o5OhEREXEIhgGbN5u9Hc6cMYdVdOsGnTqdd4hFYmIi9957LwB5eXlcddVVvP3227i7uxdru3TpUsaMGcOpU6ewWCxcf/31jB8/HicnJxYtWsSTTz5pa5uSkkKdOnXYuHEj+/btIyoqirS0tPPeyuHDhxk0aBBf/TU55pj/e4awHkP5fMMR9qdm29o1DPBiRKdQBkbXx9fTtQwvkoiIyIVz+J4PLVu2ZMuWLdx6662kpKSQmZnJ8OHD2bFjB61bt7Z3eCIiIuIINm2CuXPNwkNICNx9N3TpUqa5HSIjI1m3bh1JSUls3bqVlJQU3n///RLb+vv7M3PmTLZv386GDRtYtWoVn376KQC9evUiKSnJ9mjbti1Dhw4t962EhISwfPlyAFbsOs5/XnmJl7/dzIGzCg8AB1KzeXHBdjq+msDy34+V+3lERETKw+GLD2B+yL7yyit89913fP3114wbNw4nJyfeffdde4cmIiIi5TBv3jxatGhBZGQkTz75JIGBgezbt49du3Zx/fXX065dOyIiIop8xlssFl5++WXat29PaGgoc+fO5dVXXyUmJoYmTZrw008/AbBv3z78/Px45plnaNu2LU2aNGHlypU8+uijRMXH03rSJLY1agT/+hfJVivdu3cnOjqaVq1a8cADD2C1WkuM2cvLC1dXs+dAbm4up0+fxlLK3BBt2rShUaNGAHh4eBAVFcW+ffuKtTt8+DAJCQncfvvtRfY//vjjRERE0KpVK5YsWVLic+zbt4+AgAC2HUqn3+ARACTPeJJDUx+kICvN1s7463E6r4D4qWtVgBARkUpVLYoPZ0tISGDIkCHUrVuXZ5991t7hiIiISBmlpKRwxx138M0337B582aaN2/OiRMnKCgo4LbbbuONN95g3bp1rF69mkmTJrFu3TrbuTVq1GDNmjVMnjyZYcOGUbduXdavX88rr7zCv//9b1u79PR0oqOj2bhsGWNuvJFevXrRt29fkjZvZsQjj/D8t9+CkxN+fn58++23bNiwgS1btrBv3z6+/PLLUmPft28fkZGRBAYG4uvry3333Xfe+01OTubrr7/mhhtuKHZs2rRpXHfddQQHBxeJvUWLFmzZsoXJkyczZMiQEifdBrOo8P5PuwnsfT8AdYb+h5D4d3D29ive1jDb3zt9A+mn884bt4iIyIWoFsWHP//8kxdeeIGwsDCuvfZaAL755huSk5PtHJmIiIiU1erVq4mIiKB58+YAjBgxAjc3N3Jycvj1118ZPHgwUVFRdOrUiczMTLZv3247d9CgQQDExMSQlZXF4MGDAYiNjWXXrl22dh4eHvRv0ADef5+Y7GxquLvTvXt3s2379ra2VquVJ598ksjISNq0acP69evPuYR3aGgomzdvJjk5mZycHObMmXPOe83IyODGG2/kiSeeICYmpsgxwzCYMmUKo0aNKrLfxcWFkSNHAuaS4iEhIWzatKnE6+cVWMnNt1LWOSUNA07nFjBn48GynSAiIlJODlt8yMvL46uvvqJXr140a9aMpKQkXnvtNZycnHj66afp3bu3rQukiIiIOC7DMAgICCgyH8LevXsZMWKErY2HhwcAzn/N0XD2dn5+vtkoIwN3Jyf49lvIycE5JAQPb2/bNc5u++abb5KSksKaNWvYsmULQ4YM4cyZMwDcfPPNREVFERUVxYkTJ4rEWqNGDQYPHsyMGTNKbZuZmUnv3r3p168fo0ePLna/y5cv58yZM/Tq1eu8r43FYmH8+PG251i0aBGGYZCbX/IQkfOZtnKfVsEQEZFK4bDFh3r16vHOO+8wcOBADh06xJw5c7j55pvtHZaIiIhcoA4dOrBlyxZ27twJwPTp08nNzcXd3R0fHx+mTp1qa7t7925SU1PLfvF16+Czz6CgAFxdoXdv6N8fnEr+VejkyZPUqVMHDw8PkpOTbStHAHz99de2IkitWrXYvXs3eXnmcIXc3Fy++eYbIiIiSmx76tQpevfuTe/evXn66adLfO7JkyczcuRIWyGlUH5+Pp999hkAa9eu5fDhw0RFRTFmzBjbc/Tq1Yv003m2oRQAFjdPrDnZnI8B7E/NJi1bQy9ERKTiOexSm/n5+VgsFiwWS7EPZxEREXE8wcHBfPzxx/Tv3x93d3euueYaatSoQWBgIAsWLOCRRx7hrbfeoqCggMDAQD7//POyXbigAL77DvLyzGLDvfdCQABs21bqKQ8//DA333wzrVq1IiQkhLi4uFLbLl26lLffftvWc6Jnz54888wzJbb93//+x9q1a8nKyrINzbjlllv4v//7P8Cc12HOnDls3bq12Lm+vr5s27aNyMhI8vPz+fzzz6lZs2axdtm5BUW2fdrdxNFZT2Nxdaf2rS+WOO/D2U7l5OPv7XbONiIiIuVlMRy0b92ZM2eYPXs2kydPZvXq1fTp04dhw4YxaNAgkpKSaNmypb1DvKQyMjLw9fUlPT0dHx8fe4fjkKxWKykpKQQHB+NUyl/CpGpRzhyT8uZ4LmXOMjMzbV+o586dy9ixY/ntt98u7qJHj8LUqdCjB7RrB6WsRFFdpGblEvPij7TwN/jtpAUr5bvfTc9co+KDnejfR8ejnDke5axiled7qMO+2h4eHgwdOpSlS5eydetWWrRowUMPPUR+fj4vv/wyixcvpqCg4PwXEhERkSrjnXfeITIyktatW/Pf//7XNndCuaSmwtmTQ9auDY8+CrGx1b7wAODv5coVAV7lLDmABWgY4IWfl+bMEhGRiuewxYezXXnllbz00kvs37+f7777jpycHG644QZq165t79BERESkHJ566ik2b97Mtm3bWLVqFW3bti37yVYrrF4NH3wA8+fDkSN/H3N3r/hgqyiLxcLwjqEXdO7IzqFYLoMCjYiIXHoOO+dDSZycnOjTpw99+vTh2LFjtkmZREREpJo7fhzmzYM//zS3w8LA09O+MdnRTW3rsWDtb1gsZ808eQ5OFvBwdWZA2/qVH5yIiFyWqlXx4WxBQUElLl8lIiIi1Uhhb4elSyE/H9zcoFcvaNv2shhiURpfT1fu69aYe77ehcUC55rhq/BlmjgsGl9PDbkQEZHKUS2GXYiIiMhlyDBg+nT48Uez8HDllXDffRAdfVkXHgq1rufLxyNj8XR1xgLF5oAo3Ofp6sy0+Fi6Ng269EGKiMhlo9r2fBAREZFqzmKB5s3h0CGzt0ObNio6/EPXJoEkju3JnI0HmbZyH/tTs23HrgjwYmTnUAZG18fHQz0eRESkcqn4ICIiIo4jJQVyc6H+X3MTtGsHLVrAX8tzSnG+nq7Edw5jZKdQ0rLzOJWTTw13F/y8XDW5pIiIXDIOP+xi2bJl9g5BREREKpvVCj//DB9+CLNnmwUIMHs6qPBQJhaLBX9vNxoEeOHv7abCg4iIXFIOX3zo3bu3banNPwtnuBYREZHq4+hR+OgjSEiAggIICoK8PHtHJSIiIuXg8MWHQ4cO8cADD/D111/TqFEjevXqxZdffklu4V9ERERExDEVFMDy5TBpEhw5Yi6dOWAA3HYbeHvbOzoREREpB4cvPgQGBvLoo4+SlJTEmjVraNq0Kffddx8hISE89NBDbN682d4hioiISHmdPm32dli2zCxCNG9urmQREaFJJUVERByQwxcfzta2bVvGjh3LAw88wKlTp5gyZQrR0dF06dKFX3/91d7hiYiISFl5eICvL3h5wcCBMGiQ5nYQERFxYNWi+JCXl8fXX3/NddddR8OGDVm0aBHvvvsuR48eZffu3TRs2JBbbrnF3mGKiIjIuRw5Atl/LQVpscCNN5q9HcLD1dtBRETEwTn8UpsPPvggX3zxBYZhcPvtt/Pf//6X1q1b2457e3vz+uuvExISYscoRUREpFT5+bBiBfzyC7Rubc7rAFCjhn3jEhERkQrj8MWH7du388477zBgwADc3d1LbBMYGKglOUVERKqiw4dh7lxISTG3CwrMh7OzXcMSERGRiuXQxYe8vDwaNmxIhw4dSi08ALi4uHD11VdfwshERETknPLz4aefYNUqsFrN1Suuvx5atrR3ZCIiIlIJHHrOB1dXV2bPnm3vMERERKQ8jh+HDz80h1lYreacDvffr8KDiIhINebQxQeA/v37M3fuXHuHISIiImXl7Q1nzphzOgwebK5m4eVl76hERESkEjn0sAuAJk2a8MILL7By5Uqio6Px9vYucvyhhx6yU2QiIiJik5ICQUHmqhWennDbbeDvb/4sIiIi1Z7DFx8mT56Mn58fGzZsYMOGDUWOWSwWFR9ERETsKS8Pli6F1auhf3+IjDT3axUqkQpjGAYns/PIysnH290Ffy9XLFqeVkSqGIcfdrF3795SH3v27LF3eCIiIpev/fvhgw8gMREMA44csXdEl43g4GD+/PNPe4dxXiNHjmTChAkXdQ2LxUJaWlq5z3vuuec4c+ZMuc/bu3cv0dHRREVF0bp1a2655RZOnjxZavsFCxbQvHlzmjRpwoABA8jIyDjvdbZu3UrXrl1p3rw5rVu35o477uD06dPFrv3kU09jsViIefRj2r64mC7/XUbbFxfT7bWfmPLLXtJP55X7/kREKovDFx9ERESkisnNhR9+gGnTIDUVfHxg6FDo3dvekYnYPP/88xdUfAgJCeGXX34hKSmJbdu2ERISwnPPPVdi21OnTjFq1Cjmzp3Lrl27CAkJ4cUXXyz1Os8//zwAHh4evPvuu+zYsYPNmzeTlZXFf/7znyLXnvj1It77ahHOPsEcSS96HwdSs3lxwXY6vprA8t+PlfseRUQqQ7UoPhw8eJD333+fMWPGMHr06CIPERERuYT+/NPs7bBmjdnboW1buO8+aNLE3pFVa/Pnz6dFixZERETwxBNP2Pbv2rWL66+/nnbt2hEREcG7775rO7Zu3Tp69OhBTEwMbdq04auvvgJg3759+Pn58fjjjxMREUGrVq1YsmQJAMeOHePaa68lPDyciIgI4uPjbdd7/fXXiY2NpW3btvTu3Zv9+/cD5tLoY8aMITY2lqioKG699dYSewr8/PPPtGzZkvXr15d4j9999x3t2rUjMjKSqKgo1qxZYzv2/vvvExsbS1hYGFOnTrXtf/zxx2nXrh1RUVF07dqVnTt3AnDPPfcA0KVLF6KiokhJSSnyXDNmzOCGG26wbRuGQaNGjdi8eTPu7u54/jVXSUFBAVlZWaUOcfjhhx9o06YNzZs3B+C+++7jiy++ADjndZo0aUJERAQAzs7OtGvXjn379tmuu2jzfh5+6EH8r72/xOc1/nqczisgfupaFSBEpEpw+OJDQkICzZo144MPPuCNN95g2bJlTJ06lSlTppCUlHRB13zvvfcIDQ3Fw8OD9u3bs3bt2lLbduvWDYvFUuxx/fXX29qMHDmy2PHe//jrT2pqKkOHDsXHxwc/Pz9GjRrFqVOnLih+ERERu7FaIS0NfH1h2DDo2xc8POwdVbWWkpJCfHw8s2fPZsuWLTRu3JgTJ05QUFDA0KFDeeONN1i3bh2rV69m0qRJrFu3jrS0NO666y5mzJjB+vXrWbx4MY899hiHDh0CID09nRYtWrBlyxYmT57MkCFDyMzMZPr06YSFhbF161a2bNnCG2+8AcDnn3/Ozp07SUxMZOPGjQwdOpT77rsPgNdeew1vb2/Wrl1LUlIS4eHhPP3000XuYdasWTz44IN89913xMTEFLvH33//nfj4eD777DM2b97MunXrbF/owfwiv3btWn744Qceeugh8vPzAXjyySdZt24dSUlJ3HfffTz88MMATJw4ETALHklJSQQHBxd5vgEDBrB69WqSk5MB+Omnn/D39yfyrzlLcnNziYqKIjAwkF27dtl6LPzTgQMHaNiwoW07NDSUI0eO2OL753VK6kGRlZXFxx9/TL9+/czcnM7jtn89SM021+FcM6jE5y1kGGYR4t7pGzQEQ0TszuEnnBw7diyPP/44zz//PDVr1mT27NkEBwczdOjQYl/wy2LWrFmMHj2aiRMn0r59eyZMmECvXr3YuXNnsQ8mgDlz5pCbm2vbPnHiBJGRkdxyyy1F2vXu3btIJd7d3b3I8aFDh3LkyBEWL15MXl4e8fHx3HXXXXz++eflvgcREZFLKjMTatY0f27YEG6+GRo3hn981knlWL16NREREbRs2RKAUaNG8eCDD5KTk8Ovv/7K4MGDbW0zMzPZvn07x44dY8+ePfTp06fItXbu3EmjRo1wcXFh5MiRAHTo0IGQkBA2bdpEhw4deOutt3jsscfo2rWr7XetuXPnsm7dOqKjowHzL/mF5s6dS3p6OrNnzwbML9yhoaG245999hnOzs4sW7YMf3//Eu9x8eLF9O7d21ZwcHV1xdfX13Z86NChADRv3hwXFxeSk5OpX78+ixcv5p133iEzMxOr1UpqamqZXlNPT08GDhzIZ599xr///W+mTZtWpJeHm5sbSUlJ5Obm8uCDD/Lhhx8W6XFSVv+8zqRJkxgxYoTteG5uLoMGDeLaa6/lpptuAuCFiV9w5mQywT3vLtNzGAaczi1gzsaDxHcOK3eMIiIVxeF7Pvz2228MHz4cABcXF06fPk2NGjV44YUXio2NK4s333yTO++8k/j4eFq2bMnEiRPx8vJiypQpJbYPCAigTp06tsfixYvx8vIqVnxwd3cv0u7sD9fffvuNhQsX8vHHH9O+fXuuuuoq3nnnHWbOnMnhw4fLfQ8iIiKXRE4OLFgAb79tzu1QqFUrFR7s6OwhAAEBASQlJdkee/fuZcSIERiGQatWrYocO3DgAD169DjndTt27EhSUhLt27dnzpw5tGvXjoKCAgzDYOzYsbZrbd26la1btwLmkIV33nnHdmz79u18//33tutGRESQmppqaw8wfvx4oqKiiIqKYtGiRee9Z4+zetc4OzuTn5/PgQMHeOCBB5g+fTrbtm1j5syZpc7xkJaWZnu+wi/5d9xxB1OnTuXUqVMsWLCAIUOGFDvPzc3N1iMDzCXeC6+zdetWrrjiCtvwEzCHtNStWxcXF5cSrzN9+nTbvry8PAYNGkTdunX53//+Z3stv5q/kNyjf3Dwgzs4+MEdFGQeJ+Xr58jevYZzmbZyH4ZhnLONiEhlcvieD97e3raeB3Xr1uWPP/6gVatWABw/frxc18rNzWXDhg2MHTvWts/JyYm4uDgSExPLdI3JkyczePBgvL29i+z/6aefCA4Oxt/fnx49evDSSy9Rq1YtABITE/Hz8yvSzTAuLg4nJyfWrFlj+xA8W05ODjk5ObbtwpmTrVYrVqu17DctNlarFcMw9Po5EOXMMSlvjqfEnP3xB3z7LZb0dACM33+H2Fg7RXh5a9++PVu2bGH79u00b96cjz/+mNzcXFxdXfHx8WHy5Mm2v9rv3r2bgIAAOnTowN69e/nxxx+Ji4sDICkpiZYtW2K1WsnPz+eTTz5h5MiRrF27lsOHDxMREcEff/xBvXr1uPnmm7n22mupU6cOGRkZ9O3bl7feeosBAwYQEBBAXl4e27Zto02bNvTr148333yTTp064eXlRXZ2Nnv37qVVq1YYhkFkZCSPP/44/fr14+2336Z379488cQTRXoShIWF8cILL9juMS8vj+zsbFvvh3/+/mO1WklPT8fV1ZXatWtTUFDAO++8YzsGULNmTU6ePImPjw8+Pj5s3LixyPnt2rUD4LHHHqNnz574+flhtVrZv38/QUFBeHl5YbVa+fLLLwkPD8dqtRZbueOKK67g/vvvt8X93nvvMWjQoFKv07p1awzDIDc3lyFDhuDv78/EiRMxDMNcTjMrF5f2Q7ii/d+FkD8/GEXwgP/DvXYjzEEWJfszNYuTp3Lw83Yr7/9icg76THM8ylnFKs/r6PDFhw4dOvDLL7/QokULrrvuOh577DG2bt3KnDlz6NChQ7mudfz4cQoKCqhdu3aR/bVr12bHjh3nPX/t2rVs27aNyZMnF9nfu3dvBgwYQFhYGH/88QdPPfUUffr0ITExEWdnZ5KTk4sN6XBxcSEgIMA21vCfXn311RLHFx47duyCZm6Wv39RMQwDJyeH7xR0WVDOHJPy5niK5Cw3F/fly3H966/UVl9fcnr3pqBBA/jHpH1y6bzxxhv069cPNzc3unfvjr+/P9nZ2UyePJnnnnuON954g4KCAgICAnj//fepW7cun3zyCS+88AKPPvooeXl51KtXj6lTp3LixAl8fHxYt24db7zxBvn5+bz77rucPn2ab7/9lg8//NDWu+CZZ54hJyeHa665hv3793P11VcDkJ+fz2233Ua9evUYOXIkqamptGvXztYr4/777ycoKIgzZ86QmZlJrVq1mD59OsOGDWPMmDFFJnsE8PHx4c033+S2224jPz8fJycn/vvf/9KmTRvA/P2n8I9RVquVEydO0KBBA2688UZatmyJv78/vXv3xjAM2+SSd999N3FxcXh6ejJz5kwCAwOLva4333wzL774IjNmzLCd9/PPPzN+/Hjbc4WHh/P8888Xm7Sy0GuvvUbfvn0pKCigWbNmvP3226SkpJR4nWeffZa0tDRmz57NN998Q8uWLW3zTLRr147RTz1HC/+iBYZkJ2jkY+Djf/5eDYeSj5JbU72SKpI+0xyPclaxMjMzy9zWYjh4/6s9e/Zw6tQpIiIiyMrK4rHHHmPVqlU0adKEN998s8gkP+dz+PBh6tWrx6pVq+jYsaNt/xNPPMHy5cuLzKpckrvvvpvExES2bNly3pivvPJKlixZQs+ePXnllVf45JNPbDMwFwoODub555/n3nvvLXaNkno+NGjQwFbBl/KzWq0cO3aMoKAg/UPkIJQzx6S8OR5bztLTcfruOyx/9bYzYmOhZ09w019Sq5qLeZ/t27ePtm3blnl+BKk458vbyaxc2r285IKvv/7/4tTzoYLpM83xKGcVKyMjA39/f9LT08/7PdThez40atTI9rO3t7dt9uILERgYiLOzM0ePHi2y/+jRo9SpU+ec52ZlZTFz5kxeeOGF8z5Po0aNCAwMZPfu3fTs2ZM6deoUq5bn5+eTmppa6vO6u7sXm7QSzGEiehNdOIvFotfQwShnjkl5czwWiwWnw4dxysyEWrWgXz8s5Sjwy6V3oe+zwvZ6f9rHufIWUMOdBgHeHEjNPscAixKuCVwR4IV/DfdSlwWVC6fPNMejnFWc8ryG1ebVzs3N5eDBgxw4cKDIozzc3NyIjo4mISHBts9qtZKQkFCkJ0RJvvrqK3Jychg2bNh5n+fgwYOcOHGCunXrAtCxY0fS0tLYsGGDrc3SpUuxWq20b9++XPcgIiJSoc5a0YkuXSAuDu6911zVQqql0NBQ0tLS7B2GlMBisTCiU+gFnTuyc6gKDyJiVw5ffPj999/p0qULnp6eNGzYkLCwMMLCwggNDSUsrPzLCY0ePZqPPvqITz75hN9++417772XrKws20RNw4cPLzIhZaHJkyfTv39/2ySShU6dOsW///1vVq9ezb59+0hISKBfv340btyYXr16AdCiRQt69+7NnXfeydq1a1m5ciUPPPAAgwcPJiQk5AJeFRERkYt0+jR88w1MmQKFyyY6O8NVV4Grq31jE7mMDYyuj6ebM2WtIzhZwNPNmQFt61duYCIi5+Hwwy7i4+NxcXFhwYIF1K1b96IruoMGDeLYsWOMGzeO5ORkoqKiWLhwoW0SygMHDhTrWrJz505++eUXfvzxx2LXc3Z2ZsuWLXzyySekpaUREhLCtddey4svvlhk2MSMGTN44IEH6NmzJ05OTgwcOJC33377ou5FRETkguzYYS6heeoUWCywbx/UrGnvqEQE8PV05YNh0cRPXQsWONfsbYW/Fk8cFo2vp4qGImJfDj/hpLe3Nxs2bKB58+b2DsWuMjIy8PX1LdNEH1Iyq9VKSkoKwcHBGv/lIJQzx6S8VWHZ2fDDD/DXShYEBkL//lhDQpQzB6P3mWMqT96W/36Me6dv4HSu2TPp7F/oC/8U5+nmzMRh0XRtGlQ5AYveaw5IOatY5fke6vA9H1q2bMnx48ftHYaIiIhj++03s7dDVpb559LOnaFbN3BxAa2FLlLlXN00iMSxPZmz8SDTVu5jf2q27dgVAV6M7BzKwOj6+Hiox4OIVA0OX3z4z3/+wxNPPMErr7xCeHg4rv8Yh6peACIiIudhGJCYaBYegoKgf3+oV8/eUYnIefh6uhLfOYyRnUJJy87jVE4+Ndxd8PNy1eSSIlLlOHzxIS4uDoCePXsW2W8YBhaLhYLCSbJERESkKKsVnJzMng79+sHmzdC1q9nbQUQchsViwd/bDX9vN3uHIiJSKof/7WLZsmX2DkFERMSxZGXBd9+Bjw/07m3uq1ULevSwb1wiIiJSbTl88eHqq6+2dwgiIiKOwTBg2zZzUsnsbHPpzE6dzCKEiIiISCVyyOLDli1baN26NU5OTmzZsuWcbSMiIi5RVCIiIlXYqVPmhJI7dpjbdeqYczuo8CAiIiKXgEMWH6KiokhOTiY4OJioqCgsFgslrRiqOR9EROSyZxiwZQssXAinT5u9Hbp2hauuMn8WERERuQQcsviwd+9egoKCbD+LiIhIKbKzzWEWZ85A3bpmb4fate0dlYiIiFxmHLL40LBhwxJ/FhEREczeDoXL7Hl7Q58+kJ4OnTurt4OIiIjYhUMWH8524sQJatWqBcCff/7JRx99xOnTp+nbty9dunSxc3QiIiKXWEaGObdDu3bQpIm5LzLSvjGJiIjIZc9hiw9bt27lxhtv5M8//6RJkybMnDmT3r17k5WVhZOTE2+99RZff/01/fv3t3eoIiIilc8wICnJnNshJweOH4crrwQnJ3tHJiIiIoLD/kbyxBNPEB4ezooVK+jWrRs33HAD119/Penp6Zw8eZK7776b8ePH2ztMERGRypeeDjNmwLx5ZuGhfn247TYVHkRERKTKcNieD+vWrWPp0qVEREQQGRnJpEmTuO+++3D66xetBx98kA4dOtg5ShERkUpkGLBxI/z4o1l0cHGBHj2gQwcVHkRERKRKcdjiQ2pqKnXq1AGgRo0aeHt74+/vbzvu7+9PZmamvcITERGpfAcOwLffmj83aAD9+kFgoH1jEhERkYtiGAYns/PIysnH290Ffy9XLIUTSTswhy0+AMUSUB0SIiIiUmYNG0LbthAcDLGx6u0gIiKXzFNPPcWcOXNwd3fH1dWVl19+mV69epXYNjQ0FHd3dzw9PQEYO3YsgwYNKtbuxIkT9OzZ07adnZ3Nnj17SElJISAggPj4eFauXImnpyc1atRgwoQJtGvXDoBu3brxyCOPlGnOv8cee4w77rijyHOVx7Rp0+jQoQPNmze/oPNLs2DREhK2HWRjQUP2p2bb9jcM8GJEp1AGRtfH19O1Qp/zUnLo4sPIkSNxd3cH4MyZM9xzzz14e3sDkJOTY8/QREREKt7Jk+YQi+uvhxo1zH19+9o3JhERuSx16dKFZ555Bk9PTzZv3kzXrl05fPiw7fvYP82aNYuoqKhzXrNWrVokJSXZtl9//XWWL19OQEAAADfddBMfffQRLi4uLFiwgFtuuYV9+/aVO/Y33niD4ODgcp9XaNq0afj5+ZVafCgoKMC5nEtbL//9GPGvTCMnK5NacXcVOXYgNZsXF2zn9R938sGwaK5uGnTBsduTw/6JZMSIEQQHB+Pr64uvry/Dhg0jJCTEth0cHMzw4cPtHaaIiMjFMwxYswbefx9++w0WLbJ3RCIiYkfz58+nRYsWRERE8MQTTxAYGMikSZO49tprAcjIyMDV1ZVJkyYB8Omnn3LHHXcAkJyczK233kpsbCzh4eE8/fTTtuuGhoYybtw4OnbsSFhYGC+99FKpMfTp08fWkyE8PBzDMDh27FiF3ufkyZMZNWqUbbtv3764uJh/P+/QoQOHDh0iPz/fdjwhIYF27drRuHFjHnvsMQzDKPG6AwYMYO7cuYD5B+27776bnj170rRpUwYMGEBubi4A3377LREREURFRdG6dWvmzZvHxx9/zPr163n00UeJiori+++/Z9q0aXTv3p2BAwcSHh7O2rVr6datm+05AG6++WamTZsGQHp6Ov/6179o3bo1kZGRXDdwCEPGzyR1w3dkbf+JQ1MfJG3lF7Zzjb8ep/MKiJ+6luW/V+zrfKk4bM+HqVOn2jsEERGRypeaaq5isX+/uR0aCt272zUkERGxn5SUFOLj4/n5559p2bIlkyZN4sSJE3Tp0oUxY8aQk5PDsmXLaNeuHUuWLOGuu+5i8eLF9OnTBzD/iPvUU09x9dVXk5+fzw033MBXX33FLbfcAkBaWhqJiYkcP36cK6+8kvj4eOrVq3fOmKZOnUqjRo1o2LBhqW2GDx+OYRjExsYyfvx4goLO/df7VatWcfLkSW644YYSj//vf//juuuusxUjALZv386qVavIy8uja9eufPHFFwwZMuSczwOQlJTEsmXLcHd3p2vXrsyePZvbbruNp59+mg8//JCOHTtitVrJyMjAz8+P6dOnFxniMW3aNNasWcOmTZto1qzZeZ/vkUcewdPTky1btpCZU0C7p2fj5laTmlF9sJ7JIuAfPR8KGQZggXunbyBxbE+HG4LhsD0fREREqjWrFVavhg8+MAsPbm7mcIsRI+Cv7qciInL5Wb16NREREbRs2RKAUaNG4ebmhqenJ1FRUaxcuZIlS5YwZswYNm7ciNVqZenSpfTo0YOsrCwSEhJ4+OGHiYqKIiYmht27d7Nz507b9Qu/rAcGBtKoUSP27t17zngSEhJ4/vnnmTVrVqlz8K1YsYItW7awceNGAgMDGTFixHnvc/LkyQwfPrxIcaHQ9OnT+fLLL209OwoNHz4cV1dXvLy8GDZsGEuWLDnv84A5nMPLywtnZ2diY2P5448/AOjZsycPP/ww//3vf9myZQt+fn6lXqNTp05lKjwALFiwgMcffxwnJydmbzhInmtNSumkUYxhwOncAuZsPFi2E6oQFR9ERESqotWrYeFCyMuDsDC4915o1w40ubKIiJzl7C/8cXFxLFmyhBUrVtCzZ0/Cw8OZPn06/v7+1KlTxzYMYfXq1SQlJZGUlMTu3buLDL3w8PCw/ezs7Ex+fj7bt28nKiqKqKgo7r//ftvx5cuXEx8fz7fffnvOL95XXHEFAK6urjzyyCP8/PPPgDkcpPC6Z/dsP3XqFF9++aVtqMjZZs2axfPPP8/ixYupXbv2eV+b0mI/W0n3DPDmm28ydepUvLy8GDFiBP/9739Lfa4ahXMx/cXFxYWCggLb9pkzZ4qdYxgGn6zad857KM20lftKHVZSVan4ICIiUhXFxEDt2nDDDTB8OJy1nLSIiFy+OnbsyJYtW9ixYwcAU6ZMsc1REBcXx+eff46fnx/e3t7ExcUxbtw44uLiAPMLcvfu3Rk/frzteocPH+bgwXP/Fb1ly5a2YsV7770HmL0Zbr/9dubNm0dkZGSp52ZlZZGWlmbb/uKLL2jTpg1g9lQovG58fLytzaxZs4iMjCw2oeOXX37J008/zZIlS2wFjbNNnz6dvLw8Tp8+zeeff05cXFyJsZfVjh07aNWqFQ888AD33nsvq1evBsDHx4f09PRzntu4cWPWrFkDwN69e/nll19sx/r27cvrr7/OiVM57E/NJj/bvJaTmxfWnOwSr3c2A9ifmk1adl657sfeVHwQERGpCo4fN3s6FP4Vw80N7r7bLEKot4OIiPwlKCiIKVOmcNNNNxEZGcmuXbuoVasWADExMaSnp9uWkLzmmmvYv39/kSUlZ8yYwe7du2ndujXh4eEMGDCAEydOlDuOUaNGkZOTQ3x8vK1nwdatWwFzQsx//etfABw9epTu3bsTERFBeHg4y5cv59NPPz3ntf850WShoUOHcubMGfr162d7zrNjb9GiBZ07dyY8PJwuXbowePDgct/X2Z566ilatWpFmzZt+Oyzz3juuecAuOuuu3jllVdsE06W5IknnmDZsmWEh4czduxY2rdvbzv21ltvkZOTQ6d2bTk89UHSln8CgGfTjuSm7DH3nTXhZGlO5eSft01VYjEcra+GlCgjIwNfX1/S09Px8fGxdzgOyWq1kpKSQnBwME5Oqss5AuXMMSlv/2C1wqpV8NNPkJ9v9nSIibF3VEUoZ45HOXNMypvjqSo5CwwMZP369YSGhtotBkdRVXIGkJqVS9sXF1/w+ZueuQZ/b7cKjKj8yvM91GFXuxAREXF4KSnmShaHDpnbTZqYDxEREan2/L1caRjgxYHUbMrTI8ACXBHghZ+XY612oeKDiIjIpWa1wsqVZm+HggLw8IDevSEyUkMsRESk3I4fP27vEOQCWCwWRnQK5cUF28t97sjOoaWuLlJVqT+XiIjIpTZvHiQkmIWHpk3hvvsgKkqFBxERkcvMwOj6eLo5l/lXACcLeLo5M6Bt/coNrBKo+CAiInKpxcaCtzfcdBPcdhtorh4REZHLkq+nKx8Mi8bC+f8GUXh84rBofD0da8gFqPggIiJS+ZKTISnp7+169eCRRzTMQkRERLi6aRBT42PxdHU2ixD/OF64z9PVmWnxsXRtGnTpg6wAmvNBRESkshQUwIoV8PPPZpEhJASCg81jro73FwsRERGpHFc3DSJxbE/mbDzItJX72J+abTt2RYAXIzuHMjC6Pj4ejvv7g4oPIiIileHIEZg7F44eNbdbtAAvL7uGJCIiIlWXr6cr8Z3DGNkplLTsPE7l5FPD3QU/L1eHm1yyJCo+iIiIVKT8fLO3wy+/mKtaeHnB9ddDy5YaYiEiIiLnZbFY8Pd2w9/bzd6hVCgVH0RERCqK1QpTpsDhw+Z2q1Zw3XXm5JIiIiIilzEVH0RERCqKk5NZcEhP/7u3g4iIiIio+CAiInJRDh40iw4hIeZ2x47Qpo3mdxARERE5i4oPIiIiFyIvD376CVatglq14J57wMXFLESo8CAiIiJShIoPIiIi5fXnn+ZKFidOmNshIeZEky76WBUREREpiX5LEhERKau8PFi6FFavBsOAmjXhhhugWTN7RyYiIiJSpan4ICIiUhaZmTB1KqSmmttRUdCrF3h62jUsEREREUeg4oOIiEhZ1KgBfn7m8Iobb4QmTewdkYiIiIjDUPFBRESkNPv3Q+3a4OEBFgvcdBO4uprbIiIiIlJmTvYOQEREpMrJzYXvvzeHWSxe/Pf+mjVVeBARERG5AOr5ICIicra9e2HePEhL+3ufYZg9H0RERETkgqj4ICIiApCTY/ZyWL/e3Pbzg759oVEju4YlIiIiUh2o+CAiInLkCMycCenp5na7dhAXB+7u9o1LREREpJpQ8UFERMTHB/LywN/f7O0QFmbviERERESqFRUfRETk8nTkCNSta/7s7Q3DhkFgILi52TcuERERkWpIq12IiMjl5cwZc0LJDz+EX3/9e39IiAoPIiIiIpVEPR9EROTy8fvv8O23kJlprl5x7Ji9IxIRERG5LKj4ICIi1d/p07BwIWzebG7XqgX9+sEVV9g3LhEREZHLhIoPIiJSve3eDXPnwqlTZm+Hjh2he3dwdbV3ZCIiIiKXDRUfRESkerNYzMJDYKDZ26FBA3tHJCIiInLZUfFBRESqn/R08PU1f77ySrj1VmjaFFz0sSciIiJiD1rtQkREqo+sLPj6a3j/fbMAUahlSxUeREREROxIv4mJiEj1sH07fPedWYBwcoJ9+yAy0t5RiYiIiAgqPoiIiKPLyjKLDtu3m9vBwdC/P4SE2DUsEREREfmbig8iIuK4fv3VLDxkZ5u9Hbp0ga5dwdnZ3pGJiIiIyFlUfBAREcd1+LBZeKhTx1zJom5de0ckIiIiIiVQ8UFERByHYUBODnh4mNvdukHNmtCunXo7iIiIiFRhWu1CREQcQ2YmzJwJ06eD1Wruc3WFDh1UeBARERGp4tTzQUREqjbDgM2bYeFCOHPGLDQcOgQNGtg7MhEREREpI/V8KMF7771HaGgoHh4etG/fnrVr15batlu3blgslmKP66+/HoC8vDyefPJJwsPD8fb2JiQkhOHDh3P48OEi1wkNDS12jfHjx1fqfYqIVHkZGfD55zB3rll4CAmBu+5S4UFERETEwajnwz/MmjWL0aNHM3HiRNq3b8+ECRPo1asXO3fuJDg4uFj7OXPmkJuba9s+ceIEkZGR3HLLLQBkZ2ezceNGnnnmGSIjIzl58iQPP/wwffv2Zf369UWu9cILL3DnnXfatmvWrFlJdykiUsUZBiQlwaJFf/d26N4dOnUyV7UQEREREYei4sM/vPnmm9x5553Ex8cDMHHiRL777jumTJnCmDFjirUPCAgosj1z5ky8vLxsxQdfX18WL15cpM27775LbGwsBw4c4IorrrDtr1mzJnXq1KnoWxIRcTyGAevWmYWHevWgf38ICrJ3VCIiIiJygVR8OEtubi4bNmxg7Nixtn1OTk7ExcWRmJhYpmtMnjyZwYMH4+3tXWqb9PR0LBYLfn5+RfaPHz+eF198kSuuuIIhQ4bw6KOP4uJScopycnLIycmxbWdkZABgtVqxFk7EJuVitVoxDEOvnwNRzhxTqXkzDPNR2LOhb1/YtQs6djT3Kc92o/ea41HOHJPy5niUM8ejnFWs8ryOKj6c5fjx4xQUFFC7du0i+2vXrs2OHTvOe/7atWvZtm0bkydPLrXNmTNnePLJJ7ntttvw8fGx7X/ooYdo27YtAQEBrFq1irFjx3LkyBHefPPNEq/z6quv8vzzzxfbf+zYMc6cOXPeWKU4q9VKeno6hmHgpG7dDkE5c0wl5c2Sno77jz9irVOH3C5d/m7cpAkcP26nSKWQ3muORzlzTMqb41HOHI9yVrEyMzPL3FbFhwo0efJkwsPDiY2NLfF4Xl4et956K4Zh8MEHHxQ5Nnr0aNvPERERuLm5cffdd/Pqq6/i7u5e7Fpjx44tck5GRgYNGjQgKCioSFFDys5qtWKxWAgKCtI/RA5COXNMRfJmscCGDbB4MZbcXIzMTLjuOvD0tHeYcha91xyPcuaYlDfHo5w5HuWsYnl4eJS5rYoPZwkMDMTZ2ZmjR48W2X/06NHzzsWQlZXFzJkzeeGFF0o8Xlh42L9/P0uXLj1vgaB9+/bk5+ezb98+mjVrVuy4u7t7iUUJJycnvYkugsVi0WvoYJQzx2SxWHBKT8dpwQLYu9fc2bAhln794BzD1sR+9F5zPMqZY1LeHI9y5niUs4pTntdQr/ZZ3NzciI6OJiEhwbbParWSkJBAx44dz3nuV199RU5ODsOGDSt2rLDwsGvXLpYsWUKtWrXOG0tSUhJOTk4lrrAhIuLQDAPXTZtg4kSz8ODqCn36QHw8lOHfRxERERFxPOr58A+jR49mxIgRxMTEEBsby4QJE8jKyrKtfjF8+HDq1avHq6++WuS8yZMn079//2KFhby8PG6++WY2btzIggULKCgoIDk5GTBXynBzcyMxMZE1a9bQvXt3atasSWJiIo8++ijDhg3D39//0ty4iMilkpmJ24oVWNzcICzMnFjyHysHiYiIiEj1ouLDPwwaNIhjx44xbtw4kpOTiYqKYuHChbZJKA8cOFCsa8nOnTv55Zdf+PHHH4td79ChQ8yfPx+AqKioIseWLVtGt27dcHd3Z+bMmTz33HPk5OQQFhbGo48+WmROBxERh2YYYLGYP/v4kNO9O96+vlhiY//eLyIiIiLVlsUwDMPeQcjFy8jIwNfXl/T0dE04eYGsVispKSkEBwdr/JeDUM4cxIkTMG8edO8OYWHKmwNSzhyPcuaYlDfHo5w5HuWsYpXne6hebRERqRxWKyQmwgcfwIEDsHCh2QNCRERERC47GnYhIiIV7/hxs7fDn3+a21deCTfeaA6xUAFCRERE5LKj4oOIiFScwt4Oy5ZBfj64u0OvXtCmjeZ2EBEREbmMqfggIiIVZ/duWLzY/LlxY7O3g6+vfWMSEREREbtT8UFERCpOkyYQFQWhoRAZqd4OIiIiIgJowkkREbkYR4/CjBmQnW1uWyzQv79ZgFDhQURERET+ouKDiIiUX0EBLF8OkybBrl2QkGDviERERESkCtOwCxERKZ/kZHMliyNHzO1mzaBbN7uGJCIiIiJVm4oPIiJSNgUF8PPPsGKFuaqFpydcdx20bq0hFiIiIiJyThp2ISIiZbNiBfz0k1l4aNEC7r8fwsOrbOHhqaeeonnz5kRGRhITE8OiRYtKbRsaGkqzZs2IiooiKiqKWbNmldjuxIkTtjZRUVE0bdoUFxcXUlNTbddJSkoqU3zXXXcdO3fuLPd9FZowYQLJyckXfH5p5s6dy+rVqzEMg9SsXP5MzeZkVi6GYVT4c4mIiMjlQz0fRESkbDp2hJ07oUsXaNmyyhYdCnXp0oVnnnkGT09PNm/eTNeuXTl8+DDe3t4ltp81axZRUVHnvGatWrWKFBdef/11li9fTkBAQLnj+/7778t9ztkmTJhAt27dqFOnTrFjVqsVACen8v+N4cvZczD8G3J4xWn2p5oTiTphcHUDN7pEnGZgTAN8PV0vKnYRERG5/Kjng4iIlOzwYVi4EAr/4u3hAXffDa1anbfwMH/+fFq0aEFERARPPPEEgYGBTJo0iWuvvRaAzMxM3N3dmTRpEgCffvopd9xxBwDJycnceuutxMbGEh4eztNPP227bmhoKOPGjaNjx46EhYXx0ksvlRpDnz598PT0BCA8PBzDMDh27NgFvxwlmTx5MqNGjSqyb8aMGURHR9O4cWNee+21Us89u5dEt27dePzxx+nSpQtXXnkl99xzj63dxx9/TMuWLYmKiiI8PJw1a9bwwgsvcPjwYQYNGkRUVBRJSUk899xzDBw4kF69etG6dWuOHDlSrCdGTEwMP/30EwCHDh3i5ptvJjw8nIiICJ555hn+89EXzPr6G76a+j6Jb4wic/PfvUWOncrh5e+20/HVBJb/XrGvo4iIiFR/Kj6IiEhR+fnm6hUffwyrV8OWLX8fK0Nvh5SUFOLj45k9ezZbtmyhcePGnDhxgi5durB+/XpycnJYuXIl7dq1Y8mSJQAsXryYuLg4AEaMGMH999/P2rVr2bRpE+vXr+err76yXT8tLY3ExETWrVvHa6+9xqFDh84b09SpU2nUqBENGzYstc3w4cMJDw9n1KhRZSpSrFq1ipMnT3LDDTcU2X/06FHWr1/P6tWreeedd1i1atV5rwXwxx9/sGzZMrZt28aiRYtITEwE4LHHHiMhIYGkpCQ2btxIq1atGDduHCEhIcyaNYukpCRbj43ExEQ+/fRTtm/fTr169c75fMOGDSM6OpqtW7eyZcsWYq4fwod7fPBs3B6f2IGExL9DzchetvbGX4/TeQXET12rAoSIiIiUi4oPIiLyt0OH4MMPzYklrVZzTocmTcp1idWrVxMREUHLli0BGDVqFG5ubnh6ehIVFcXKlSv5+eefeeKJJ9i4cSNWq5WlS5fSo0cPsrKySEhI4OGHHyYqKoqYmBh2795dZG6EIUOGABAYGEijRo3Yu3fvOeNJSEjg+eefZ9asWVhKKZ6sWLGCLVu2sHHjRgIDAxkxYsR573Py5MkMHz4cF5eiIxhHjRqFxWIhMDCQAQMG2Aos5zNo0CBcXFxsr9Mff/wBQM+ePbn99tv53//+x969e6lRo0ap17juuuuoXbv2eZ/r1KlT/PLLLzz22GMApJ/OY+z3+ynLrA6GYRYh7p2+gfTTeWW5NREREREVH0REBMjLg8WLzd4Ox45BjRowaBAMHAheXhd16bO/8MfFxZGQkMDq1avp2bMn4eHhTJ8+HX9/f+rUqWOb1HD16tUkJSWRlJTE7t27iwy98PDwsP3s7OxMfn4+27dvt00Cef/999uOL1++nPj4eL799luaNWtWaoxXXHEFAK6urjzyyCP8/PPPgDkcpPC6U6dOtbU/deoUX375pW2oyPnuf8mSJbbrvPzyyyW2K+m+AGbPns348ePJy8vjuuuuY+bMmaU+1z8LEy4uLhQUFNi2z5w5U+J5szcc5HRuAWWdU9Iw4HRuAXM2HizbCSIiInLZU/FBRETg669h5UrzW2VEBNx3n7mixQXo2LEjW7ZsYceOHQBMmTKF3NxcwCw+fPHFF/j4+ODt7U1cXBzjxo2zDbmoUaMG3bt3Z/z48bbrHT58mIMHz/0lt2XLlrZixXvvvQeYvRluv/125s2bR2RkZKnnZmVlkZaWZtv+4osvaNOmDWAOxSi8bnx8vK3NrFmziIyMpHnz5sWuN23aNABSU1P55ptv6NmzJ3Fxcbbr/N///d857+Vs+fn5/PHHH8TExPD4449z8803s3btWgB8fHxIT08/5/mNGzdmzZo1AKxdu9bWg6RGjRp07dqVN954A8Mw+GTVPgqyzWs5uXthzckqU3zTVu7TKhgiIiJSJio+iIgIdO4MPj5w220wYMBF9XYICgpiypQp3HTTTURGRrJr1y5q1aoFmBMepqen06VLFwCuueYa9u/fT8+ePW3nz5gxg927d9O6dWvCw8MZMGAAJ06cKHcco0aNIicnh/j4eFuvg61btwLmhJj/+te/AHOOhu7duxMREUF4eDjLly/n008/Pee1S5po8uz7j46OJjY2lgceeIBOnTqVO/ZCBQUF3HHHHbRu3ZqoqCg2bNjA6NGjAXjooYe48847bRNOluSll17ivffeIzIykilTptCqVSvbsc8++4z169fTomUrEt8YRcaGBQB4t+pO9o5fODz1oSITTv6TAexPzSYtW0MvRERE5Pwshv5kUS1kZGTg6+tLeno6Pj4+9g7HIVmtVlJSUggODr6g5enk0lPOLsKBA3DyJJzdIyA/H1wqZwXmwMBA1q9fT2hoqPJWxfyZmk2X/y47ZxsnDFr4G/x20oKVovNm/PxEdxoEXNzQHKl4ep85JuXN8Shnjkc5q1jl+R5aOb9liohI1ZSbC0uXwpo1ZqGhfn34q1dCZRUepGrzdr+4vNe4yPNFRKoTwzA4mZ1HVk4+3u4u+Hu5ljrZscjlRr8xiIhcLvbtg3nzzB4PAK1bg7f3JXnq48ePX5LnkfLz93KlYYAXB1Kzy7TaRSELcEWAF35erpUVmojIRfm///s/5s+fj7OzMwBjxoxh8ODB5W771FNPMWfOHNzd3XF1deXll1+mVy9zKeL4+HiuvPJKHnzsSb7ZdJhPVu1jf2q27boNA7wY0SmUgdH18fXUv5dyedOwi2pCwy4unrpgOR7lrIxyc2HJEvhrokJ8fKBvX2jc2C7hKG9Vz5Rf9vLigu2lFh9KGnZhAcbd2JL4zmGXLE4pO73PHJPyVrHS0tLw8/MD4NChQ7Ro0YI9e/YQGBhYrrY//PAD3bp1w9PTk82bN9O1a1cOHz6Mt7c3I0aMwDuwHiu8u5CVawUo8m9pYZ8HTzdnPhgWzdVNgyrvhqVM9D6rWOX5HqpXW0SkOisogA8//LvwEB0N999vt8KDVE0Do+vj6eZMWXsGO1nMX6QHtK1fuYGJSLUxf/58WrRoQUREBE888QSBgYFMmjSJa6+9FjC/wLi6ujJp0iTAXOq4cDnj5ORkbr31VmJjYwkPDy+y/HJoaCjjxo2jY8eOhIWF8dJLL9mOFRYTwFwi2TAMrFZrifGdq22fPn3w9PQEIDw8HMMwOHbsGABHM3JYsHwtu6eM5uCkuzj23ZtY83Js1zL+epzOKyB+6lqW/37swl5AkWpAxQcRkerM2RnCw8HXF26/HW68Edzd7R2VVDG+nq58MCwaC5y3AFF4fOKwaHUhFpEySUlJIT4+ntmzZ7NlyxYaN27MiRMn6NKlC+vXrycnJ4dly5bRrl07lixZAsDixYttyzCPGDGC+++/n7Vr17Jp0ybWr1/PV199Zbt+WloaiYmJrFu3jtdee41Dhw7Zjr399ts0a9aMtm3bMmnSJIKDg0uNsyxtp06dSqNGjWjYsCHpp/NYs+cEJ//8nTq3PE/Ivz7AejqTzPXzip1nGGYR4t7pG0g/rVWC5PKk4oOISHWzZw8kJ/+93aUL3HcfXHml/WKSKu/qpkFMjY/F09XZLEL843jhPk9XZ6bFx9JVXYdFpIxWr15NREQELVu2BMylkN3c3PD09CQqKoqVK1eyZMkSxowZw8aNG7FarSxdupQePXqQlZVFQkICDz/8MFFRUcTExLB792527txpu/6QIUMAc2WlRo0asXfvXtuxhx56iJ07d7Jq1SpeeeWVcy7dfL62CQkJPP/888yaNQuLxcLsDQfJt1qpG3EVTu5eWJycqRFxLaf3JZV4fcOA07kFzNl48EJfShGHpgknRUSqi5wc+PFH2LAB6tSBO+80ez4UPkTO4+qmQSSO7cmcjQeZtrLopGlBNdwZ2LkxA2Ma4OOhHg8icuHOXv0hLi6OJUuWsGLFCsaPH094eDjTp0/H39+fOnXqcOrUKcAsYHh4eJR4vbP3Ozs7k5+fX6xNZGQk9erV46effqJFixa2gkXnzp157733Sm07cOBAAJYvX058fDzffvstzZo1wzAMPlm1r5T7O/f9T1u5j5GdQrUKhlx2VHwQEakOdu+Gb7+F9HRz+4orwGpV0UHKzdfTlfjOYYzsFEpadh6ncvLxdnUi59RJateurcm5RKTcOnbsSHx8PDt27KB58+ZMmTKF3NxcwCw+3HrrrTRs2BBvb2/i4uIYN24c/fv3B6BGjRp0796d8ePH89xzzwFw+PBhrFYr9eufe96Z7du323pb/PHHH2zatImWLVvSokULkpKSytQWYMWKFdx+++3MmzePyMhIAE5m59kKtEe2rqJW+E0YLu6c2roEj4ZRpcZkAPtTs0nLzsPf262sL6FItaDig4iIIztzBhYtgk2bzO2AAHMli9BQu4Yljs9iseDv7Ya/t5s5M3iW/kInIhcmKCiIKVOmcNNNN+Hm5kbv3r2pVasWADExMaSnp9OzZ08ArrnmGh544AHbNsCMGTMYPXo0rVu3xmKx4O3tzYcffnje4sMTTzzB3r17cXV1xcXFhXfffZcWLVqUu+2oUaPIyckhPj7e1v61dyfZfvar35jkL5+lIDsd93rNqRnT77yvyamcfBUf5LKjpTarCS21efG07I7juexzdvIkTJkCmZlmH8/27aFHD3Cr2r/MXPZ5c0DKmeNRzhzT5ZS3wMBA1q9fT6iDFstTs3Jp++LiEpciLotNz1yj4oOdXE7vs0uhPN9D1fNBRMRR+fmZPR3c3KBfP3OohYiIiFQ6fy9XGgZ4cTA1q1znWYArArzw89LcOXL5UfFBRMSR7NoFDRuaBQeLBW6+GTw8wFW/xIiIiOM4fvy4vUO4KBaLhRGdQnl5wa/lPndkZ002KZcn9TMREXEE2dkwZw7MmAFLl/69v2ZNFR5ERETsYGB0fTzdnMs82MLJAp5uzgxoe+65KkSqKxUfRESqut9+g/ffhy1bzN4OLi7mYuEiIiJiN76errw7NBoL519es/D4xGHR+HrqjwZyedKwCxGRqiorC374AbZtM7eDgsy5Hc4zu7eIiIhcGl2bBJIf15RH5u0hK9cKmMtpFiqsSXi6OjNxWDRdmwZd8hhFqgoVH0REqqL9++HLL80ChJMTdO4MV19t9noQERGRKqN1PV9+frIHc5MOM23lPvanZtuOXRHgxcjOoQyMro+Ph3o8yOVNv8WKiFRFAQFQUADBwdC/P4SE2DsiERERKYWvpyvxncMY2SmUtOw8TuXkU8PdBT8vV00uKfIXFR9ERKoCw4CDB6FBA3O7Zk0YMcIcaqHeDiIiIg7BYrHg7+2Gv7ebvUMRqXI04aSIiL2dOmUOsZg8GX7//e/9deuq8CAiIiIi1YJ+qxURsRfDgK1bzUklT58253Y4edLeUYmIiIiIVDgVH0RE7CEzExYsgJ07ze26dc2VLOrUsW9cIiIiIiKVQMUHEZFLbft2mD8fzpwBZ2dzFYvOnc2fRURERESqIRUfREQuNWdns/AQEmL2dqhd294RiYiIiIhUKhUfREQqm2FAWhr4+5vbzZrB4MHQtKk5z4OIiIiISDWn33pFRCpTejrMmAGTJpnzPBRq3lyFBxERERG5bKjng4hIZTAM2LgRfvwRcnLMJTMPHTKLDiIiIiIilxkVH0REKlpaGnz7Lfzxh7ndoIE5t0NgoF3DEhERERGxFxUfREQq0oYNsGgR5OaavR169oT27TXEQkREREQuayo+iIhUpKNHzcLDFVeYvR1q1bJ3RCIiIiIidqfig4jIxTAMc9lMT09zOy7OXDqzTRv1dhARERER+YuKDyIiFyo1FebNA4sFRoww/+vmBtHR9o5MRERERKRKUfFBRKS8DAPWrIGEBMjLMwsOKSlmjwcRERERESlGxQcRkfI4ccLs7XDggLkdFgZ9+4K/v33jEhERERGpwlR8EBEpC6v1794O+flmb4drrzWHWFgs9o5ORERERKRKU/FBRKQsDAOSkszCQ6NGZm8HPz97RyUiIiIi4hBUfBARKY3Vav7XyQmcnc2lM48cgbZt1dtBRERERKQcVHwQESnJsWMwdy40awZdu5r7QkLMh4iIiIiIlIuKDyIiZ7NaYeVK+OknKCiAtDTo0MGc40FERERERC6Iig8iIoVSUszeDocPm9tNm8INN6jwICIiIiJykVR8EBEpKDB7Oyxfbv7s4QF9+kBEhOZ2EBERERGpACo+iIikp8OKFWbhoVkzs7dDzZr2jkpEREREpNpQ8UFELk+G8XevhoAA6N0b3N2hdWv1dhARERERqWBO9g6gKnrvvfcIDQ3Fw8OD9u3bs3bt2lLbduvWDYvFUuxx/fXX29oYhsG4ceOoW7cunp6exMXFsWvXriLXSU1NZejQofj4+ODn58eoUaM4depUpd2jyGXtyBGYNAn+/PPvfTExEB6uwoOIiIiISCVQ8eEfZs2axejRo3n22WfZuHEjkZGR9OrVi5SUlBLbz5kzhyNHjtge27Ztw9nZmVtuucXW5r///S9vv/02EydOZM2aNXh7e9OrVy/OnDljazN06FB+/fVXFi9ezIIFC1ixYgV33XVXpd+vyGWloACWLYOPPjILEIsX2zsiEREREZHLgooP//Dmm29y5513Eh8fT8uWLZk4cSJeXl5MmTKlxPYBAQHUqVPH9li8eDFeXl624oNhGEyYMIGnn36afv36ERERwaeffsrhw4eZO3cuAL/99hsLFy7k448/pn379lx11VW88847zJw5k8OFs+6LyMU5fBg+/NCcVNJqhZYt4dZb7R2ViIiIiMhlQXM+nCU3N5cNGzYwduxY2z4nJyfi4uJITEws0zUmT57M4MGD8fb2BmDv3r0kJycTFxdna+Pr60v79u1JTExk8ODBJCYm4ufnR0xMjK1NXFwcTk5OrFmzhptuuqnY8+Tk5JCTk2PbzsjIAMBqtWK1Wst34wKYr51hGHr9HEiZcpafbxYcVq3CYrVieHubK1m0alV4kUsTrNjoveZ4lDPHo5w5JuXN8Shnjkc5q1jleR1VfDjL8ePHKSgooHbt2kX2165dmx07dpz3/LVr17Jt2zYmT55s25ecnGy7xj+vWXgsOTmZ4ODgIsddXFwICAiwtfmnV199leeff77Y/mPHjhUZziFlZ7VaSU9PxzAMnJzUKcgRlCVnLjt24LFoEQB5zZuT26MHhpcXlDKUSiqf3muORzlzPMqZY1LeHI9y5niUs4qVmZlZ5rYqPlSgyZMnEx4eTmxsbKU/19ixYxk9erRtOyMjgwYNGhAUFISPj0+lP391ZLVasVgsBAUF6R8iB1GmnAUFwYkT0Lw5tGhxaQOUEum95niUM8ejnDkm5c3xKGeORzmrWB4eHmVuq+LDWQIDA3F2dubo0aNF9h89epQ6deqc89ysrCxmzpzJCy+8UGR/4XlHjx6lbt26Ra4ZFRVla/PPCS3z8/NJTU0t9Xnd3d1xd3cvtt/JyUlvootgsVj0GjqYYjn7809zUslbb4XCfwwHDrRfgFIivdccj3LmeJQzx6S8OR7lzPEoZxWnPK+hXu2zuLm5ER0dTUJCgm2f1WolISGBjh07nvPcr776ipycHIYNG1Zkf1hYGHXq1ClyzYyMDNasWWO7ZseOHUlLS2PDhg22NkuXLsVqtdK+ffuKuDWR6i8vDxYtgilTYM8ec54HERERERGpEtTz4R9Gjx7NiBEjiImJITY2lgkTJpCVlUV8fDwAw4cPp169erz66qtFzps8eTL9+/enVq1aRfZbLBYeeeQRXnrpJZo0aUJYWBjPPPMMISEh9O/fH4AWLVrQu3dv7rzzTiZOnEheXh4PPPAAgwcPJiQk5JLct4hDO3AAvv3WHF4BEBkJXbvaNyYREREREbFR8eEfBg0axLFjxxg3bhzJyclERUWxcOFC24SRBw4cKNa1ZOfOnfzyyy/8+OOPJV7ziSeeICsri7vuuou0tDSuuuoqFi5cWGR8zIwZM3jggQfo2bMnTk5ODBw4kLfffrvyblSkOsjNxW3pUvj9d3O7Zk248UZo2tS+cYmIiIiISBEWwzAMewchFy8jIwNfX1/S09M14eQFslqtpKSkEBwcrPFfDsL6/fdkL12Kt7c3lrZtoVevv+d5kCpL7zXHo5w5HuXMMSlvjkc5czzKWcUqz/dQ9XwQEcfVpQsFv/6K0a8fFvV2EBERERGpslTqERHHsXcvLFwIhR22vL05PWwYNG5s37hEREREROSc1PNBRKq+nBxYsgTWrTO3GzaEFi3Mny0W+8UlIiIiIiJlouKDiFRte/bA/PmQlmZux8RAo0Z2DUlERERERMpHxQcRqZpycuDHH2HDBnPbzw/69YOwMLuGJSIiIiIi5afig4hUTZ9/Dvv3mz/HxkJcHLi52TcmERERERG5ICo+iEjVdPXVsGAB9O0LoaH2jkZERERERC6Cig8iUjXs2gVnzkB4uLndqBHcfz84O9s3LhERERERuWgqPoiIfZ0+DYsWQVKSOayiQQNzfgdQ4UFEREREpJpQ8UFE7GfnTnNoRWamuWRmdDR4e9s7KhERERERqWAqPojIpXf6NPzwA2zZYm7XqgX9+5u9HkREREREpNpR8UFELq3cXPjgA8jIMHs7dOoE3bqBq6u9IxMRERERkUqi4oOIXFpubhARYQ656NcP6te3d0QiIiIiIlLJVHwQkcq3fTsEBZkPMHs6dOsGLvonSERERETkcqDf/EWk8mRlwfffw6+/Qr16MGoUODmp6CAiIiIicpnRNwARqXiGYRYcvv8esrPNgsOVV5r7RURERETksqPig4hUrFOn4Lvv4LffzO06dcy5HerWtW9cIiIiIiJiNyo+iEjFSUmBqVPNpTSdnKBrV+jSBZyd7R2ZiIiIiIjYkYoPIlJxAgMhIAAKCqB/f7PXg4iIiIiIXPZUfBCRC2cY5vCKpk3NSSSdnGDwYPDyUm8HERERERGxcbJ3ACLioDIy4Isv4Msv4aef/t5fs6YKDyIiIiIiUoR6PohI+RgGJCXBokVw5oxZaPD0tHdUIiIiIiJShan4ICJll54O334Lu3eb2/XqmXM7BAXZNSwREREREanaVHwQkbLZtQu+/hpycsz5Hbp3h44dzXkeREREREREzkHFBxEpm6Agc8hF/fpmb4fAQHtHJCIiIiIiDkLFBxEpmWHAgQPQsKG57ecHd9wBwcHq7SAiIiIiIuWibxAiUtzJk/DppzB1KuzZ8/f+OnVUeBARERERkXJTzwcR+ZthwLp1sGQJ5OaCqytkZto7KhERERERcXAqPoiIKTUV5s+HffvM7YYNoV8/CAiwa1giIiIiIuL4VHwQEdi4EX74AfLyzN4O11wD7dqBxWLvyEREREREpBpQ8UFEwM3NLDyEhUHfvuDvb++IRERERESkGlHxQeRyZLWak0rWqmVut2oF7u7QuLF6O4iIiIiISIXTtPUil5vjx81VLKZMgexsc5/FAk2aqPAgIiIiIiKVQj0fRC4XViskJsKyZZCfb/Z0OHrUHGohIiIiIiJSiVR8ELkcHDsG8+bBwYPmduPGcOON4Otr37hEREREROSyoOKDSHVmGLBypdnboaAAPDygVy+IitIQCxERERERuWRUfBCpziwWOHHCLDw0aWL2dvDxsXdUIiIiIiJymVHxQaS6KSiA3Fzw9DS3e/WCRo2gdWv1dhAREREREbtQ8UGkOjl6FObOBW9vGDrULDZ4eEB4uL0jExERERGRy5iKDyLVQUEB/Pyz+SgoMHs9pKWBv7+9IxORCmAYBiez88jKycfb3QV/L1cs6skkIiIiDkTFBxFHd+SIuZJFcrK53bw53HAD1Khh37hEqpGRI0cSFRXFI488wsSJE8nMzOTf//43SUlJ7Nixg8GDB1fo802ZMoW33nqL3377jVvuG8vh+j3Yn5ptO94wwIsRnUIZGF0fX0/XCn1uERERkcrgZO8AROQCFRSYq1h89JFZePDygptvhkGDVHgQqUT33HMP//73vwFISkpi5syZFf4c0dHR/Pu/E6nRshuLfj3KgbMKDwAHUrN5ccF2Or6awPLfj1X484uIiIhUNBUfRByV1Qrbtpn/bdkS7rtPk0pKtWaxWHjllVeIjY0lLCyMqVOn2o6tX7+eTp06ERERQWxsLCtXrgTgzjvv5PXXX7e127t3L3Xq1CEvL4+EhAQ6duxImzZtaNWqFZMnTy5THM899xyPPPIIKSkpjBs3jmXLlhEVFcU999wDwLp16+jRowcxMTG0adOGr776qsTrNG3alPXr19u2p02bxk033QRAmmcIz61II98wjxn/ONf463E6r4D4qWtVgBAREZEqT8MuRBxJfj44OZkPV1fo3x8yMqBVK3tHJnJJuLu7s3btWnbs2EG7du24/fbbsVqtDBgwgI8++ohevXrxyy+/MHDgQHbv3k18fDx33XUXjz/+OGB+wR86dCiurq60bduWX375BWdnZ1JTU2nTpg29evWifv36ZYolODiYF154gblz5zJ37lwA0tLSuOuuu/j++++pW7cux48fp23btnTq1Il69eoVOX/kyJFMmzaNmJgYAKZOncrjjz9O+uk87p2+oVjBoSSGAVjg3ukbSBzbU0MwREREpMpSzwcRR3HoEHz4Iaxe/fe+Bg1UeJDLytChQwFo3rw5Li4uJCcns3PnTpycnOjVqxcAV111FbVr1yYpKYlOnTqRn5/PunXrMAyDTz/9lPj4eABOnDjBLbfcQuvWrenRowcnTpxg27ZtFxXfqlWr2LNnD3369CEqKoq4uDgAdu7cWazt8OHDmTVrFjk5OezZs4fff/+dPn36MHvDQU7nFpiFhTIwDDidW8CcjQcvKnYRERGRyqSeDyJVXX6+ObfDqlXmt4y1a6F9e3B2tndkIpech4eH7WdnZ2fy8/NLbHf2ShDx8fFMnTqVU6dOERgYSOvWrQFz7obrrruO2bNnY7FYaNu2LWfOnCEtLY1u3boBEBYWxjfffFPm+AzDoFWrVqxatarYsfHjx9vmh/jPf/5Dr169iImJYd68efz6668MGzYMZ2dnPlm1r8zPd7ZpK/cxslOoVsEQERGRKknFB5Gq7OBBmDsXjh83tyMioHdvFR5EztKsWTOsViuLFy/mmmuuYdWqVSQnJxMVFQXA7bffTmRkJCdOnOCOO+6wnXfy5EkaNmyIxWJhxYoVbN68GQA/Pz+SkpLK9Nw+Pj6kp6fbtjt16sTevXtZsmSJrddDUlISLVu2ZMyYMYwZM6bI+fHx8UyZMoWdO3fy/fffczI7r8iqFmVlAPtTs0nLzsPf263c54uIiIhUNg27EKmK8vLgxx9h8mSz8FCjBgweDAMGmKtaiIiNm5sbc+bM4dlnnyUiIoJHHnmEr7/+mhp/rfoSEhJCbGws8+fP57bbbrOdN378eMaMGUNUVBRTpkyhffv25X7unj17kpOTQ0REBPfccw/+/v589913vPLKK0RGRtqKDlartcTz+/Xrx7p166hduzYtWrQgK8fsyXFq6xIOvjeC7J2/kLbycw6+N4Lco3+cN55TOSX3BBERERGxN4thlHVUqVRlGRkZ+Pr6kp6ejo+Pj73DcUhWq5WUlBSCg4NxcrJzXS4lxZzfoaAAIiPN3g6envaNqQqqUjmTMlPeSpealUvbFxdf8PmbnrmmUno+KGeORzlzTMqb41HOHI9yVrHK8z1Uwy5Eqgqr1VzFAiA4GHr1Aj8/aNrUrmGJyKXj7+VKwwAvDqRml2m1i0IW4IoAL/y8tNqFiIiIVE0q9YhUBfv3w/vvw5Ejf++LjVXhQeQyY7FYGNEp9ILOHdlZk02KiIhI1aXig4g95ebC99/D1Knm3A5Ll9o7IhGxs4HR9fF0c6asdQQnC3i6OTOgbf3KDUxERETkIqj4IGIve/fCBx+YS2cCtG0LAwfaNyYRsTtfT1c+GBaNBc5bgCg8PnFYNL6eGnIhIiIiVZfmfBC51HJyYMkSWLfO3Pb1hb594cor7RuXiFQZVzcNYmp8LPdO38Dp3AKAInNAFNYkPF2dmTgsmq5Ngy55jCIiIiLloeKDyKX2669/Fx5iYuCaa8Dd3b4xiUiVc3XTIBLH9mTOxoNMW7mP/anZtmNXBHgxsnMoA6Pr4+OhHg8iIiJS9an4IHKptWljTjAZGQmNGtk7GhGpwnw9XYnvHMbITqGkZedxKiefGu4u+Hm5anJJERERcSgqPohUtt274ZdfYMgQcHMzB2nfdJO9oxIRB2KxWPD3dsPf283eoYiIiIhcEBUfRCrLmTOwaBFs2mRur1oF3brZNSQRERERERF7UPFBpDLs2gXffgsZGeZ2+/bQqZN9YxIREREREbETFR9EKtLp02Zvh6QkczsgAPr1g4YN7RqWiIiIiIiIPan4IFKRfvzRLDxYLNChA/ToAa6aiV5ERERERC5vTvYOoCp67733CA0NxcPDg/bt27N27dpztk9LS+P++++nbt26uLu707RpU77//nvb8dDQUCwWS7HH/fffb2vTrVu3YsfvueeeSrtHqSQ9ekD9+nDHHdCrlwoPIiIiIiIiqOdDMbNmzWL06NFMnDiR9u3bM2HCBHr16sXOnTsJDg4u1j43N5drrrmG4OBgvv76a+rVq8f+/fvx8/OztVm3bh0FBQW27W3btnHNNddwyy23FLnWnXfeyQsvvGDb9vLyqvgblIq1Y4e5bGavXuZ2zZowapTZ80FEREREREQAFR+KefPNN7nzzjuJj48HYOLEiXz33XdMmTKFMWPGFGs/ZcoUUlNTWbVqFa5//ZU7NDS0SJugoKAi2+PHj+fKK6/k6quvLrLfy8uLOnXqlCnOnJwccnJybNsZf01saLVasVqtZbqGFGW1WjEMo2yvX3Y2/PADlm3bADAaNYIrr/z7uGFUUpRytnLlTKoM5c3xKGeORzlzTMqb41HOHI9yVrHK8zqq+HCW3NxcNmzYwNixY237nJyciIuLIzExscRz5s+fT8eOHbn//vuZN28eQUFBDBkyhCeffBJnZ+cSn2P69OmMHj0ayz/+Oj5jxgymT59OnTp1uPHGG3nmmWdK7f3w6quv8vzzzxfbf+zYMc6cOVOe25a/WK1W0tPTMQwDJ6fSRyQ579yJe0ICTtnZGBYLee3akevpCSkplzBagbLnTKoW5c3xKGeORzlzTMqb41HOHI9yVrEyMzPL3FbFh7McP36cgoICateuXWR/7dq12bFjR4nn7Nmzh6VLlzJ06FC+//57du/ezX333UdeXh7PPvtssfZz584lLS2NkSNHFtk/ZMgQGjZsSEhICFu2bOHJJ59k586dzJkzp8TnHTt2LKNHj7ZtZ2Rk0KBBA4KCgvDx8SnnnQuY/xBZLBaCgoJK/ocoKwu+/x7L9u1gsWCEhUHfvlCv3qUPVoAy5EyqJOXN8Shnjkc5c0zKm+NRzhyPclaxPDw8ytxWxYeLZLVaCQ4OZtKkSTg7OxMdHc2hQ4d47bXXSiw+TJ48mT59+hASElJk/1133WX7OTw8nLp169KzZ0/++OMPrjy7O/9f3N3dcXd3L7bfyclJb6KLYLFYSn4NDQNmzIDkZHB2hquuwtK1K7joLWRvpeZMqjTlzfEoZ45HOXNMypvjUc4cj3JWccrzGuqb01kCAwNxdnbm6NGjRfYfPXq01LkY6tati6ura5EhFi1atCA5OZnc3Fzc3Nxs+/fv38+SJUtK7c1wtvbt2wOwe/fuEosPcolZLNC9OyxdCv36wT+KRyIiIiIiIlI6lXrO4ubmRnR0NAkJCbZ9VquVhIQEOnbsWOI5nTt3Zvfu3UUm2vj999+pW7dukcIDwNSpUwkODub6668/byxJSUmAWdwQOzAM2LoVfv31733NmsHdd6vwICIiIiIiUk4qPvzD6NGj+eijj/jkk0/47bffuPfee8nKyrKtfjF8+PAiE1Lee++9pKam8vDDD/P777/z3Xff8corr3D//fcXua7VamXq1KmMGDECl3901f/jjz948cUX2bBhA/v27WP+/PkMHz6crl27EhERUfk3LUVlZsKsWTB7Nnz7rbldSF2zREREREREyk3DLv5h0KBBHDt2jHHjxpGcnExUVBQLFy60TUJ54MCBIuNaGjRowKJFi3j00UeJiIigXr16PPzwwzz55JNFrrtkyRIOHDjAHXfcUew53dzcWLJkCRMmTCArK4sGDRowcOBAnn766cq9WSnKMHD59VdYtw7OnDHndujYEUpZcURERERERETKxmIYhmHvIOTiZWRk4OvrS3p6ula7uBAZGVjnzyc7KQlvb28sISHQvz/8Y+UTqVqsVispKSkEBwdrwiAHorw5HuXM8Shnjkl5czzKmeNRzipWeb6HqueDyOnT8MEHWLKzMZycMHr0wHLVVWbPBxEREREREbloKj6IeHpCRATGgQOc7twZ75YtNbeDiIiIiIhIBVLxQS4/hgGbNkFoKAQEmPuuuQYA6/Hj9otLRERERESkmlLxQS4v6ekwfz788Qc0bAgjR4LFAi4ucNZyqSIiIiIiIlJxVHyQy4NhwIYNsHgx5OSYxYZmzcz9Fou9oxMREREREanWVHyQ6i8tzeztsGePuX3FFdCvH9SqZdewRERERERELhcqPkj1dugQfPIJ5OaCqyv07AmxsZpQUkRERERE5BJS8UGqtzp1wN8fPDzM3g6FE0yKiIiIiIjIJaPig1QvhgFbtkDr1uDsbD5uvx28vTW3g4iIiIiIiJ2o+CDVx4kT5twO+/eb8zxcfbW5v0YNu4YlIiIiIiJyuVPxQRyf1Qpr1sDSpZCXB25uKjiIiIiIiIhUISo+iGM7fhzmzYM//zS3GzWCvn3Bz8+uYYmIiIiIiMjfVHwQx/Xrr/DNN5CfD+7ucO210Lat5nYQERERERGpYlR8EMdVp47538aN4cYbwdfXvvGIiIiIiIhIiVR8EMdhtcK+febQCoBateCuuyAoSL0dREREREREqjAnewcgUiYpKTB5Mnz2GRw48Pf+4GAVHkRERERERKo49XyQqq2gAFauhOXLzZ89PCAry95RiYiIiIiISDmo+CBV19GjMHcuHDlibjdtas7tULOmXcMSERERERGR8nGIYRdPPfUUzZs3JzIykpiYGBYtWlRq29DQUJo1a0ZUVBRRUVHMmjWr0uI6deoUlgro8r9o0SJbvFFRUYSEhNC2bVsA9u3bh185lo3s3bv3BceRlpbG+PHjL/j8c5kwYQLJyckYhkFqVi5/pmaTmpWLYRgln5CYCJMmmYUHT08YMABuu02FBxEREREREQfkED0funTpwjPPPIOnpyebN2+ma9euHD58GG9v7xLbz5o1i6ioqEsbZDnk5+fj4vL3S9+rVy969epl277hhhvo3r37BV174cKFFxxXYfFhzJgxJR7/Z9zl8eZbE0jzbcKyY17sT8227W8Y4MWITqEMjK6Pr6fr3yd4eJjDLJo3h+uvV9FBRERERETEgZW758P8+fNp0aIFERERPPHEEwQGBjJp0iSuvfZaADIyMnB1dWXSpEkAfPrpp9xxxx0AJCcnc+uttxIbG0t4eDhPP/207bqhoaGMGzeOjh07EhYWxksvvWQ71qdPHzw9PQEIDw/HMAyOHTt24Xf9l8TERK666ioiIyOJiIhg3rx5AKxfv55OnToRERFBbGwsK1eutJ3z4Ycf0qRJE9q0acNbb71V5Hrr1q2jR48exMTE0KZNG7766ivg794LTz75JG3btuXdd98tNabDhw+TkJDA7bffXmT/448/TkREBK1atWLJkiWlnn/FFVfYfrZYLLzyyivExsYSFhbG1KlTAbBarTzwwAO0aNGCyMhIoqOjOXPmDPfccw+ZmZlERUURExMDQLdu3XjooYfo2LEj1157bbGeGP/s/VHSa3rHQ0/y58FDvPz43SS+MYrco3ts7Q+kZvPigu10fvlHVq7e8feNREXB8OEwaJAKDyIiIiIiIg6uXH/GTklJIT4+np9//pmWLVsyadIkTpw4QZcuXRgzZgw5OTksW7aMdu3asWTJEu666y4WL15Mnz59ABgxYgRPPfUUV199Nfn5+dxwww189dVX3HLLLYD5l/fExESOHz/OlVdeSXx8PPXq1SsSw9SpU2nUqBENGzYsNc7hw4djGAaxsbGMHz+eoKCgYm1SU1Pp378/X3/9NV26dMFqtZKWlkZubi4DBgzgo48+olevXvzyyy8MHDiQ3bt3s2/fPp599lk2bdpE3bp1eeqpp2zXS0tL46677uL777+nbt26HD9+nLZt29KpUycA0tPTadWqFf/5z3/O+RpPmzaN6667juDgYNu+9PR0WrRoweuvv87q1avp27cvf/zxBzXL8KXc3d2dtWvXsmPHDtq1a8ftt9/O1q1bSUhI4Ndff8XJyYn09HTc3NyYOHEiUVFRJCUlFbnG77//zooVK3B1dWXfvn2lPldJr+l363fzk7cLzjWmEdT3SdxqNypyjgEEnUql167V/JSYQ8GEZ+ka3sBcwaJRo5KfSERERERERBxKuXo+rF69moiICFq2bAnAqFGjcHNzw9PTk6ioKFauXMmSJUsYM2YMGzduxGq1snTpUnr06EFWVhYJCQk8/PDDtr+s7969m507d9quP2TIEAACAwNp1KgRe/fuLfL8CQkJPP/888yaNavUuRZWrFjBli1b2LhxI4GBgYwYMaLEdomJiTRr1owuXbqYL4STEwEBAezcuRMnJyfbMIirrrqK2rVrk5SUxNKlS+nTpw9169YF4N5777Vdb9WqVezZs4c+ffoQFRVFXFwcgO3+XF1dGTZs2DlfX8MwmDJlCqNGjSqy38XFhZEjRwLQoUMHQkJC2LRp0zmvVWjo0KEANG/eHBcXF5KTk2nUqBH5+fnccccdfPLJJ+Tl5eHkVPr/CsOGDcPV1bXU44X++Zpm5hTw5II9lDKrA87WAjru38xtmxcRmHUSZ2sBz07+ifTTeWW6NxEREREREXEMFzXnw9kFgLi4OJYsWcKKFSsYP3484eHhTJ8+HX9/f+rUqcOpU6cAs4Dh4eFR4vXO3u/s7Ex+fr5te/ny5cTHx/Ptt9/SrFmzUmMqHHbg6urKI488QtOmTQFz+Mebb74JwMMPP1ykZ0F57rO0/YZh0KpVK1atWlWs3b59+/Dy8rJ9wX/ooYdYsWIFAJ999hnh4eG2ezxz5kyR+R/OFdP48eOZOXMmAOPGjSuxXUmvqa+vL9u2bWP58uUsW7aMsWPHsmLFilLnc6hRo4btZxcXFwoKCmzbZ86cKTXG2RsOcjq3oMTiQ+3M41yzaw2B2WkA/B7YkGWNYjjj5sGcjQeJ7xxW6nVFRERERETEsZSr50PHjh3ZsmULO3aYY/OnTJlCbm4uYBYfPv/8c/z8/PD29iYuLo5x48bZegDUqFGD7t27F1lN4fDhwxw8ePC8z7tixQpuv/125s2bR2RkZKntsrKySEtLs21/8cUXtGnTBjCHYiQlJZGUlER8fDydOnVi165d/Pzzz4A5D0JqairNmjXDarWyePFiwOzRkJycTFRUFD169GDhwoUkJycDMHHiRNtzderUib179xaZjyEpKcn2+pzt7bfftsVSWHgAmDx5MiNHjsTZ2blI+/z8fD777DMA1q5dy+HDh4mKimLMmDG26xS+zmVx7NgxsrKyuPbaa3nllVcIDQ1l+/bt+Pj4cPr06RJjLlSnTh0Mw2D79u2AWdQ5+zUofE0Nw2DaL3soOJ0JgJO7F9acLDAMOu9LYvDmHwnMTiPb1YNvm3fl++ZXcdrNLJRMW7mv9FUwRERERERExOGUq+dDUFAQU6ZM4aabbsLNzY3evXtTq1YtAGJiYkhPT6dnz54AXHPNNTzwwAO2bYAZM2YwevRoWrdujcViwdvbmw8//JD69euf83lHjRpFTk4O8fHxtn2FPQbmz5/P/Pnz+fjjjzl69CgDBw6koKAAwzBo1KhRkS/HZ/P39+ebb77hscceIzMzEycnJ1588UVuvPFG5syZw0MPPcRjjz2Gh4cHX3/9NTVq1KB169Y899xzdOnShRo1ajBgwIAi1/vuu+94/PHHeeyxx8jLy+OKK65g7ty5ZXpt09PTmTNnDlu3bi12rLCnQmRkJPn5+Xz++edlmu+hNH/++Sd33nkneXl5FBQU0LlzZ/r06YOrqyvDhw8nIiKCGjVqsH79+mLnuri48M4773DDDTdQq1Ytbr755iKvQeFrmpaewa5jWfh1GYZX4/bUjL6REwvfweLqTk7L7lgw2BEUyk+Nojnj+nfvDAPYn5pNWnYe/t5uF3yPIiIiIiIiUnVYjIv8E3NgYCDr168nNDS0gkKSC5GRkYGvry/p6en4+PjYOxz+TM2my3+XlXjMI+8MIRnH2VOr9KLTz090p0GAV2WFVyKr1UpKSgrBwcHnnANDqg7lzDEpb45HOXM8ypljUt4cj3LmeJSzilWe76F6taVSeLuX3qnmjKvHOQsPADXOcb6IiIiIiIg4lov+hnf8+PGKiEOqGX8vVxoGeHEgNbvU1S5KYgGuCPDCz+v8q2uIiIiIiIiIY1DPB6kUFouFEZ1CL+jckZ1DS11hRERERERERByPig9SaQZG18fTzZmy1hGcLODp5syAtucekiEiIiIiIiKORcUHqTS+nq58MCwaC5y3AFF4fOKwaHw9NeRCRERERESkOlHxQSrV1U2DmBofi6ers1mE+Mfxwn2ers5Mi4+la9OgSx+kiIiIiIiIVCotKSCV7uqmQSSO7cmcjQeZtnIf+1OzbceuCPBiZOdQBkbXx8dDPR5ERERERESqIxUf5JLw9XQlvnMYIzuFkpadx6mcfGq4u+Dn5arJJUVERERERKo5FR/kkrJYLPh7u+Hv7WbvUEREREREROQS0ZwPIiIiIiIiIlKpVHwQERERERERkUql4oOIiIiIiIiIVCoVH0RERERERESkUqn4ICIiIiIiIiKVSsUHEREREREREalUKj6IiIiIiIiISKVS8UFEREREREREKpWKDyIiIiIiIiJSqVR8EBEREREREZFKpeKDiIiIiIiIiFQqFR9EREREREREpFKp+CAiIiIiIiIilUrFBxERERERERGpVCo+iIiIiIiIiEilUvFBRERERERERCqVig8iIiIiIiIiUqlc7B2AVAzDMADIyMiwcySOy2q1kpmZiYeHB05Oqss5AuXMMSlvjkc5czzKmWNS3hyPcuZ4lLOKVfj9s/D76Lmo+FBNZGZmAtCgQQM7RyIiIiIiIiKXk8zMTHx9fc/ZxmKUpUQhVZ7VauXw4cPUrFkTi8Vi73AcUkZGBg0aNODPP//Ex8fH3uFIGShnjkl5czzKmeNRzhyT8uZ4lDPHo5xVLMMwyMzMJCQk5Lw9SdTzoZpwcnKifv369g6jWvDx8dE/RA5GOXNMypvjUc4cj3LmmJQ3x6OcOR7lrOKcr8dDIQ1yEREREREREZFKpeKDiIiIiIiIiFQqFR9E/uLu7s6zzz6Lu7u7vUORMlLOHJPy5niUM8ejnDkm5c3xKGeORzmzH004KSIiIiIiIiKVSj0fRERERERERKRSqfggIiIiIiIiIpVKxQcRERERERERqVQqPoiIiIiIiIhIpVLxQaqN9957j9DQUDw8PGjfvj1r164tte1HH31Ely5d8Pf3x9/fn7i4uGLtLRZLiY/XXnvN1iY0NLTY8fHjx1faPVZH5cnbnDlziImJwc/PD29vb6Kiovjss8+KtDEMg3HjxlG3bl08PT2Ji4tj165dRdqkpqYydOhQfHx88PPzY9SoUZw6dapS7q86qsic5eXl8eSTTxIeHo63tzchISEMHz6cw4cPF7mO3msXp6LfZyNHjiyWj969exdpo/fZxavovOlzrfKVJ2dnmzlzJhaLhf79+xfZr8+0yleROdNn2qVT0e81fa5dIoZINTBz5kzDzc3NmDJlivHrr78ad955p+Hn52ccPXq0xPZDhgwx3nvvPWPTpk3Gb7/9ZowcOdLw9fU1Dh48aGtz5MiRIo8pU6YYFovF+OOPP2xtGjZsaLzwwgtF2p06darS77e6KG/eli1bZsyZM8fYvn27sXv3bmPChAmGs7OzsXDhQlub8ePHG76+vsbcuXONzZs3G3379jXCwsKM06dP29r07t3biIyMNFavXm38/PPPRuPGjY3bbrut0u+3OqjonKWlpRlxcXHGrFmzjB07dhiJiYlGbGysER0dXeQ6eq9duMp4n40YMcLo3bt3kXykpqYWuY7eZxenMvKmz7XKVd6cFdq7d69Rr149o0uXLka/fv2KHNNnWuWq6JzpM+3SqIz3mj7XLg0VH6RaiI2NNe6//37bdkFBgRESEmK8+uqrZTo/Pz/fqFmzpvHJJ5+U2qZfv35Gjx49iuxr2LCh8dZbb11QzHLxeTMMw2jTpo3x9NNPG4ZhGFar1ahTp47x2muv2Y6npaUZ7u7uxhdffGEYhmFs377dAIx169bZ2vzwww+GxWIxDh06dLG3VO1VdM5KsnbtWgMw9u/fb9un99qFq4ycjRgxotgvbmfT++ziXYr3mj7XKtaF5Cw/P9/o1KmT8fHHHxd7X+kzrfJVdM5Kos+0ilcZedPn2qWhYRfi8HJzc9mwYQNxcXG2fU5OTsTFxZGYmFima2RnZ5OXl0dAQECJx48ePcp3333HqFGjih0bP348tWrVok2bNrz22mvk5+df2I1cZi42b4ZhkJCQwM6dO+natSsAe/fuJTk5ucg1fX19ad++ve2aiYmJ+Pn5ERMTY2sTFxeHk5MTa9asqajbq5YqI2clSU9Px2Kx4OfnV2S/3mvlV5k5++mnnwgODqZZs2bce++9nDhxwnZM77OLcynea/pcq1gXmrMXXniB4ODgEvOgz7TKVRk5K4k+0ypWZeZNn2uVz8XeAYhcrOPHj1NQUEDt2rWL7K9duzY7duwo0zWefPJJQkJCivxDdrZPPvmEmjVrMmDAgCL7H3roIdq2bUtAQACrVq1i7NixHDlyhDfffPPCbuYycqF5S09Pp169euTk5ODs7Mz777/PNddcA0BycrLtGv+8ZuGx5ORkgoODixx3cXEhICDA1kZKVhk5+6czZ87w5JNPctttt+Hj42Pbr/fahamsnPXu3ZsBAwYQFhbGH3/8wVNPPUWfPn1ITEzE2dlZ77OLdCnea/pcq1gXkrNffvmFyZMnk5SUVOJxfaZVrsrI2T/pM63iVVbe9Ll2aaj4IJe98ePHM3PmTH766Sc8PDxKbDNlyhSGDh1a7Pjo0aNtP0dERODm5sbdd9/Nq6++iru7e6XGfbmqWbMmSUlJnDp1ioSEBEaPHk2jRo3o1q2bvUOTUpQ1Z3l5edx6660YhsEHH3xQ5Jjea5fW+XI2ePBgW9vw8HAiIiK48sor+emnn+jZs6edopby/PuozzX7yszM5Pbbb+ejjz4iMDDQ3uFIGZQ3Z/pMqxrKmjd9rl0aKj6Iw8V/1PsAAA5USURBVAsMDMTZ2ZmjR48W2X/06FHq1KlzznNff/11xo8fz5IlS4iIiCixzc8//8zOnTuZNWvWeWNp3749+fn57Nu3j2bNmpX9Ji5DF5o3JycnGjduDPx/e3cfU1Ud/wH8fRPu5eHKQ0ACKqhwr5HjWWNISwwMweThDzIrQVvYcg6dYLNZk+k0E8ioFTJ10NRyTdQ0nxJ8KBHRaFcxGQJDXT6RKAkzn7if3x/O8+vwkKAc/f3y/dru5jnn+3C+5+O538vnnnMuEBISgtraWnzyySeIjo5W6l2+fBleXl6qNkNCQgAAnp6eaG5uVrV59+5dXL169YH/X552WsTsvvsf0s6ePYt9+/apviHqDs+13tEyZv80YsQIuLu7o6GhATExMTzPHpHWceO81v/6GrPGxkacOXMGkydPVtZZrVYA975Nraur45ymMS1i5ufnB4Bzmpa0jNs/cV7TBp/5QP/v6fV6hIeHo7y8XFlntVpRXl6OyMjIHuutWLECS5Yswe7du1X3b3W2du1ahIeHIzg4+IH7YrFY8Mwzz3S5LIu6eti4dWa1WnHr1i0AwPDhw+Hp6alq8/r166iqqlLajIyMRGtrK6qrq5Uy+/btg9VqRURExKMO6z9Ni5gB//shrb6+HmVlZXBzc3tgGzzXekermHX2xx9/oKWlRfkDiefZo9E6bpzX+l9fY/b888+jpqYGFotFeSUmJmL8+PGwWCwYOnQo5zSNaREzgHOa1rSKW2ec1zTyJJ92SdRfNm7cKAaDQUpKSuTUqVMyc+ZMcXFxkUuXLomIyLRp02TBggVK+eXLl4ter5dNmzapflKnra1N1e5ff/0lDg4OUlhY2KXPw4cPy8qVK8VisUhjY6OsX79ePDw8JC0tTdvB/of0NW7Lli2Tn376SRobG+XUqVOSl5cnNjY2snr1aqXM8uXLxcXFRX744Qc5ceKEJCUldfuzZKGhoVJVVSWHDh0Sk8nEn0rqpf6O2e3btyUxMVGGDBkiFotFdT7eunVLRHiuPar+jllbW5tkZ2dLZWWlNDU1SVlZmYSFhYnJZJKbN28q7fA8ezRavD+KcF7TUl9j1ll3T9vnnKat/o4Z57THo7/jxnnt8WHygf4zvvzyS/Hx8RG9Xi8vvviiHDlyRNk2btw4SU9PV5Z9fX0FQJfXokWLVG0WFRWJvb29tLa2dumvurpaIiIixNnZWezs7CQgIECWLVumepOiB+tL3BYuXCj+/v5iZ2cnrq6uEhkZKRs3blS1Z7Va5eOPP5ZBgwaJwWCQmJgYqaurU5VpaWmRqVOnitFoFCcnJ5kxY0aXxBP1rD9j1tTU1O25CED2798vIjzX+kN/xuzGjRvy6quvioeHh9ja2oqvr69kZGQoH/ru43n26Pr7/VGE85rW+hKzzrpLPnBO015/xoxz2uPTn3HjvPb46EREHvvlFkRERERERET01OAzH4iIiIiIiIhIU0w+EBEREREREZGmmHwgIiIiIiIiIk0x+UBEREREREREmmLygYiIiIiIiIg0xeQDEREREREREWmKyQciIiIiIiIi0hSTD0RERERERESkKSYfiIiIiJ6QnJwchISE/J9ph4iISCtMPhARET2lpk+fDp1OB51OB71eD39/fyxevBh379590rummdLSUkRHR8PZ2RlGoxFBQUFYvHgxrl69+qR3rdd0Oh22bt2qWpednY3y8vIns0NERES9wOQDERHRU2zixIm4ePEi6uvrkZWVhZycHOTm5mrW3+3btzVr+0EWLlyIKVOmYMyYMdi1axdOnjyJ/Px8HD9+HOvWrXvodu/cudNl3eMep9FohJub22Ptk4iIqC+YfCAiInqKGQwGeHp6wtfXF++//z5iY2Oxbds2AMCtW7eQnZ2NwYMHw9HREREREThw4IBSt6WlBVOnTsXgwYPh4OCAwMBAfPfdd6r2o6OjMXv2bMydOxfu7u6Ii4uDiCAnJwc+Pj4wGAzw9vZGZmamUufatWtIS0uDq6srHBwcEB8fj/r6emV7SUkJXFxcsGfPHgQEBMBoNCpJlJ4cPXoUy5YtQ35+PnJzczF27FgMGzYMEyZMQGlpKdLT05WyhYWF8PPzg16vx8iRI7skJnQ6HQoLC5GYmAhHR0csXbpUue1hzZo1GD58OOzs7AAAra2tePfdd+Hh4QEnJye88sorOH78eI/7eezYMUyYMAHu7u5wdnbGuHHj8Ntvvynbhw0bBgBISUmBTqdTljvfdmG1WrF48WIMGTIEBoMBISEh2L17t7L9zJkz0Ol02Lx5M8aPHw8HBwcEBwejsrKyx30jIiJ6FEw+EBERkcLe3l751n727NmorKzExo0bceLECaSmpmLixIlKIuDmzZsIDw/Hjh07cPLkScycORPTpk3D0aNHVW1+88030Ov1qKiowKpVq1BaWoqVK1eiqKgI9fX12Lp1KwIDA5Xy06dPx6+//opt27ahsrISIoKEhATVFQY3btxAXl4e1q1bh59//hnnzp1DdnZ2j+PasGEDjEYjZs2a1e12FxcXAMCWLVswZ84cZGVl4eTJk3jvvfcwY8YM7N+/X1U+JycHKSkpqKmpwTvvvAMAaGhoQGlpKTZv3gyLxQIASE1NRXNzM3bt2oXq6mqEhYUhJiamx9s82trakJ6ejkOHDuHIkSMwmUxISEhAW1sbgHvJCQAoLi7GxYsXleXOCgoKkJ+fj7y8PJw4cQJxcXFITExUJXGAe1eDZGdnw2KxwGw2Y+rUqf/p226IiOgJEiIiInoqpaenS1JSkoiIWK1W2bt3rxgMBsnOzpazZ8/KgAED5Pz586o6MTEx8uGHH/bY5qRJkyQrK0tZHjdunISGhqrK5Ofni9lsltu3b3epf/r0aQEgFRUVyrorV66Ivb29fP/99yIiUlxcLACkoaFBKfPVV1/JoEGDetyv+Ph4CQoK6nH7fWPHjpWMjAzVutTUVElISFCWAcjcuXNVZRYtWiS2trbS3NysrPvll1/EyclJbt68qSrr5+cnRUVFSr3g4OAe96ejo0MGDhwo27dvV/W/ZcuWLv3/sx1vb29ZunSpqsyYMWNk1qxZIiLS1NQkAGTNmjXK9t9//10ASG1tbY/7Q0RE9LB45QMREdFT7Mcff4TRaISdnR3i4+MxZcoU5OTkoKamBh0dHTCbzTAajcrr4MGDaGxsBAB0dHRgyZIlCAwMxLPPPguj0Yg9e/bg3Llzqj7Cw8NVy6mpqfj7778xYsQIZGRkYMuWLcq37bW1tbCxsUFERIRS3s3NDSNHjkRtba2yzsHBAX5+fsqyl5cXmpubexyniPTqeNTW1iIqKkq1LioqStU3AIwePbpLXV9fX3h4eCjLx48fR3t7O9zc3FTHsKmpSTmGnV2+fBkZGRkwmUxwdnaGk5MT2tvbuxzTf3P9+nVcuHChV+MICgpS/u3l5QUA/3ociYiIHpbNk94BIiIienLGjx+PwsJC6PV6eHt7w8bm3keD9vZ2DBgwANXV1RgwYICqjtFoBADk5uaioKAAn3/+OQIDA+Ho6Ii5c+d2ediio6Ojanno0KGoq6tDWVkZ9u7di1mzZiE3NxcHDx7s9X7b2tqqlnU63b8mGMxmMw4dOoQ7d+50qfswOo+pu3Xt7e3w8vJSPSfjvvu3eXSWnp6OlpYWFBQUwNfXFwaDAZGRkZo9wPKfx0Kn0wG497wIIiKi/sYrH4iIiJ5ijo6O8Pf3h4+Pj5J4AIDQ0FB0dHSgubkZ/v7+qpenpycAoKKiAklJSXj77bcRHByMESNG4PTp073q197eHpMnT8YXX3yBAwcOoLKyEjU1NQgICMDdu3dRVVWllG1paUFdXR1eeOGFhx7nm2++ifb2dnz99dfdbm9tbQUABAQEoKKiQrWtoqLiofoOCwvDpUuXYGNj0+UYuru7d1unoqICmZmZSEhIwKhRo2AwGHDlyhVVGVtbW3R0dPTYr5OTE7y9vfttHERERP2BVz4QERFRF2azGW+99RbS0tKQn5+P0NBQ/PnnnygvL0dQUBAmTZoEk8mETZs24fDhw3B1dcVnn32Gy5cvP/AP3JKSEnR0dCAiIgIODg5Yv3497O3t4evrCzc3NyQlJSEjIwNFRUUYOHAgFixYgMGDByMpKemhxxMREYEPPvgAWVlZOH/+PFJSUuDt7Y2GhgasWrUKL730EubMmYP58+fj9ddfR2hoKGJjY7F9+3Zs3rwZZWVlfe4zNjYWkZGRSE5OxooVK2A2m3HhwgXs2LEDKSkp3d66YTKZsG7dOowePRrXr1/H/PnzYW9vryozbNgwlJeXIyoqCgaDAa6url3amT9/PhYtWgQ/Pz+EhISguLgYFosFGzZs6PM4iIiI+gOvfCAiIqJuFRcXIy0tDVlZWRg5ciSSk5Nx7Ngx+Pj4AAA++ugjhIWFIS4uDtHR0fD09ERycvID23VxccHq1asRFRWFoKAglJWVYfv27XBzc1P6DQ8Px2uvvYbIyEiICHbu3PnIt0t8+umn+Pbbb1FVVYW4uDiMGjUK8+bNQ1BQkPJTm8nJySgoKEBeXh5GjRqFoqIiFBcXIzo6us/96XQ67Ny5Ey+//DJmzJgBs9mMN954A2fPnsWgQYO6rbN27Vpcu3YNYWFhmDZtGjIzM/Hcc8+pyuTn52Pv3r0YOnQoQkNDu20nMzMT8+bNQ1ZWFgIDA7F7925s27YNJpOpz+MgIiLqDzrp7ROYiIiIiIiIiIgeAq98ICIiIiIiIiJNMflARERERERERJpi8oGIiIiIiIiINMXkAxERERERERFpiskHIiIiIiIiItIUkw9EREREREREpCkmH4iIiIiIiIhIU0w+EBEREREREZGmmHwgIiIiIiIiIk0x+UBEREREREREmmLygYiIiIiIiIg09T9dv3lUj+KRtAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import zscore\n",
    "\n",
    "# Create dataframe with the model evaluation results\n",
    "data = {\n",
    "    'Model': [\n",
    "        'qwen-2.5-72b-instruct', 'gemini-2.5-flash-preview', 'qwen3-8b', \n",
    "        'gpt-4o-mini', 'qwen3-32b', 'deepseek-chat-v3-0324', \n",
    "        'claude-3.7-sonnet', 'qwen3-30b-a3b', 'gemma-3-27b-it', \n",
    "        'phi-4', 'llama-4-maverick', 'qwen-2.5-7b-instruct', \n",
    "        'nova-lite-v1', 'gpt-4.1-nano', 'qwen2.5-coder-7b-instruct'\n",
    "    ],\n",
    "    'Pearson': [\n",
    "        0.4595, 0.4427, 0.4459, 0.4243, 0.4262, 0.4035, \n",
    "        0.3898, 0.3807, 0.3762, 0.3675, 0.3587, 0.3496, \n",
    "        0.3430, 0.3422, 0.2662\n",
    "    ],\n",
    "    'Binary_Accuracy': [\n",
    "        0.7400, 0.8300, 0.8161, 0.8167, 0.7133, 0.7500, \n",
    "        0.8367, 0.8267, 0.7600, 0.7700, 0.8233, 0.7167, \n",
    "        0.6810, 0.8600, 0.6633\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Calculate z-scores to normalize both metrics\n",
    "df['Pearson_Z'] = zscore(df['Pearson'])\n",
    "df['Binary_Z'] = zscore(df['Binary_Accuracy'])\n",
    "\n",
    "# Create different weightings of the unified score\n",
    "# Equal weights\n",
    "df['Unified_Equal'] = (df['Pearson_Z'] + df['Binary_Z']) / 2\n",
    "\n",
    "# Pearson-biased (60-40)\n",
    "df['Unified_Pearson_Bias'] = 0.6 * df['Pearson_Z'] + 0.4 * df['Binary_Z']\n",
    "\n",
    "# Binary-biased (40-60)\n",
    "df['Unified_Binary_Bias'] = 0.4 * df['Pearson_Z'] + 0.6 * df['Binary_Z']\n",
    "\n",
    "# Harmonic mean (favors balanced performance)\n",
    "df['Unified_Harmonic'] = 2 / (1/df['Pearson_Z'] + 1/df['Binary_Z'])\n",
    "df['Unified_Harmonic'] = df['Unified_Harmonic'].fillna(-3)  # Handle cases where z-scores are negative\n",
    "\n",
    "# Create a product-based score (severely penalizes weakness in either metric)\n",
    "df['Pearson_Scaled'] = (df['Pearson'] - df['Pearson'].min()) / (df['Pearson'].max() - df['Pearson'].min())\n",
    "df['Binary_Scaled'] = (df['Binary_Accuracy'] - df['Binary_Accuracy'].min()) / (df['Binary_Accuracy'].max() - df['Binary_Accuracy'].min())\n",
    "df['Unified_Product'] = df['Pearson_Scaled'] * df['Binary_Scaled']\n",
    "\n",
    "# Create rankings for each unified metric\n",
    "for col in ['Unified_Equal', 'Unified_Pearson_Bias', 'Unified_Binary_Bias', 'Unified_Product']:\n",
    "    df[f'{col}_Rank'] = df[col].rank(ascending=False)\n",
    "\n",
    "# Sort by the equal-weight unified score for default ranking\n",
    "ranked_df = df.sort_values('Unified_Equal', ascending=False)\n",
    "\n",
    "# Print the rankings\n",
    "print(\"Model Rankings by Different Unified Metrics:\")\n",
    "print(ranked_df[['Model', 'Pearson', 'Binary_Accuracy', 'Unified_Equal', 'Unified_Product']].head(15))\n",
    "\n",
    "# Visualization of models in 2D space\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(df['Pearson'], df['Binary_Accuracy'], s=100)\n",
    "\n",
    "# Add labels to each point\n",
    "for i, model in enumerate(df['Model']):\n",
    "    plt.annotate(model, (df['Pearson'].iloc[i], df['Binary_Accuracy'].iloc[i]), \n",
    "                 fontsize=8, ha='right')\n",
    "\n",
    "plt.title('Model Performance: Pearson Correlation vs Binary Accuracy')\n",
    "plt.xlabel('Pearson Correlation')\n",
    "plt.ylabel('Binary Accuracy')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Add a diagonal line to show balanced performance\n",
    "plt.plot([df['Pearson'].min(), df['Pearson'].max()], \n",
    "         [df['Binary_Accuracy'].min(), df['Binary_Accuracy'].max()], \n",
    "         'r--', alpha=0.5)\n",
    "\n",
    "# Calculate the combined metric rankings\n",
    "result_table = ranked_df[['Model', 'Pearson', 'Binary_Accuracy', 'Unified_Equal', 'Unified_Product']]\n",
    "result_table = result_table.rename(columns={\n",
    "    'Unified_Equal': 'Combined Score (Equal Weight)',\n",
    "    'Unified_Product': 'Combined Score (Product)'\n",
    "})\n",
    "\n",
    "print(\"\\nFinal Unified Rankings:\")\n",
    "print(result_table.to_string(index=False))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "paper-embeddings",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
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 },
 "nbformat": 4,
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}
